{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1497e64c",
   "metadata": {},
   "source": [
    "# 时间序列分析 Time Series Analysis\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbdbba16",
   "metadata": {},
   "source": [
    "## 数据理解与预处理 "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9fc22de",
   "metadata": {},
   "source": [
    "### 数据准备"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "be6498a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\anaconda\\envs\\data_ana_project_env\\lib\\site-packages\\tqdm\\auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
      "  from .autonotebook import tqdm as notebook_tqdm\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns \n",
    "from IPython.display import display\n",
    "import prophet\n",
    "\n",
    "from sklearn.metrics import mean_absolute_percentage_error\n",
    "import sys\n",
    "import os\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "\n",
    "warnings.filterwarnings('ignore')\n",
    "plt.rcParams[\"font.family\"] = [\"SimHei\", \"Arial\"]\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "sns.set_style('whitegrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8216548a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 51207 entries, 0 to 51206\n",
      "Data columns (total 37 columns):\n",
      " #   Column                       Non-Null Count  Dtype  \n",
      "---  ------                       --------------  -----  \n",
      " 0   Customer ID                  51207 non-null  object \n",
      " 1   Customer Name                51207 non-null  object \n",
      " 2   Segment                      51207 non-null  object \n",
      " 3   City                         51207 non-null  object \n",
      " 4   State                        51207 non-null  object \n",
      " 5   Country                      51207 non-null  object \n",
      " 6   Region                       51207 non-null  object \n",
      " 7   Gender                       51207 non-null  object \n",
      " 8   Age                          51207 non-null  int64  \n",
      " 9   Education                    51207 non-null  object \n",
      " 10  Marital Status               51207 non-null  object \n",
      " 11  Order ID                     51207 non-null  object \n",
      " 12  Order Date                   51207 non-null  object \n",
      " 13  Months                       51207 non-null  object \n",
      " 14  Ship Mode                    51207 non-null  object \n",
      " 15  Product Category             51207 non-null  object \n",
      " 16  Product                      51207 non-null  object \n",
      " 17  Sales                        51207 non-null  float64\n",
      " 18  Quantity                     51207 non-null  float64\n",
      " 19  Discount                     51207 non-null  float64\n",
      " 20  Order Priority               51207 non-null  object \n",
      " 21  Browsing Time (min)          51207 non-null  float64\n",
      " 22  Like                         51207 non-null  int64  \n",
      " 23  Share                        51207 non-null  int64  \n",
      " 24  Add to Cart                  51207 non-null  int64  \n",
      " 25  Unit_price                   51207 non-null  float64\n",
      " 26  Gross_Profit                 51207 non-null  float64\n",
      " 27  COGS                         51207 non-null  float64\n",
      " 28  Shipping_Cost_Reconstructed  51207 non-null  float64\n",
      " 29  Operating_Cost               51207 non-null  float64\n",
      " 30  Net_Profit                   51207 non-null  float64\n",
      " 31  Net_Profit_Margin            51207 non-null  float64\n",
      " 32  Gross_Profit_Margin          51207 non-null  float64\n",
      " 33  age_kmeans                   51207 non-null  object \n",
      " 34  Year                         51207 non-null  int64  \n",
      " 35  Quarter                      51207 non-null  object \n",
      " 36  Season                       51207 non-null  object \n",
      "dtypes: float64(12), int64(5), object(20)\n",
      "memory usage: 14.5+ MB\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('../data/processed/cleaned_e_commerce_data.csv')\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "16c55e19",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 改格式\n",
    "df['Order Date'] = pd.to_datetime(df['Order Date'])\n",
    "df['Year'] = df['Year'].apply(lambda x:str(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "273210ba",
   "metadata": {},
   "source": [
    "### 1.1 数据加载与基本信息分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "69e805d6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== 数据基本信息 ===\n",
      "数据时间范围: 2022-01-13 00:00:00 至 2024-12-31 00:00:00\n",
      "总记录数: 51,207\n",
      "唯一客户数: 794\n",
      "\n",
      "=== 每年实际交易天数 ===\n",
      "2022年: 221天 (覆盖度: 60.5%)\n",
      "2023年: 222天 (覆盖度: 60.8%)\n",
      "2024年: 221天 (覆盖度: 60.5%)\n"
     ]
    }
   ],
   "source": [
    "# 1.1 数据加载与基本信息分析\n",
    "def load_and_analyze_data(df):\n",
    "    \"\"\"加载数据并分析基本情况\"\"\"\n",
    "    # 读取清洗后的数据\n",
    "    df['Order Date'] = pd.to_datetime(df['Order Date'])\n",
    "    \n",
    "    print(\"=== 数据基本信息 ===\")\n",
    "    print(f\"数据时间范围: {df['Order Date'].min()} 至 {df['Order Date'].max()}\")\n",
    "    print(f\"总记录数: {len(df):,}\")\n",
    "    print(f\"唯一客户数: {df['Customer ID'].nunique():,}\")\n",
    "    \n",
    "    # 计算每年实际交易天数\n",
    "    yearly_days = df.groupby(df['Order Date'].dt.year)['Order Date'].nunique()\n",
    "    print(\"\\n=== 每年实际交易天数 ===\")\n",
    "    for year, days in yearly_days.items():\n",
    "        print(f\"{year}年: {days}天 (覆盖度: {days/365*100:.1f}%)\")\n",
    "    \n",
    "    return df\n",
    "\n",
    "df = load_and_analyze_data(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc66833e",
   "metadata": {},
   "source": [
    "### 1.2 交易天数统计与数据覆盖度评估"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "b2d23bd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "<Figure size 1800x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 设置中文字体\n",
    "plt.rcParams[\"font.family\"] = [\"SimHei\",]\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "def analyze_data_coverage(df):\n",
    "    \"\"\"分析数据覆盖情况（优化布局，解决拥挤）\"\"\"\n",
    "    # 数据处理逻辑不变\n",
    "    full_calendar = pd.date_range(start='2022-01-01', end='2024-12-31', freq='D')\n",
    "    transaction_dates = set(df['Order Date'].dt.normalize())\n",
    "    transaction_flag = pd.Series(\n",
    "        [date in transaction_dates for date in full_calendar], \n",
    "        index=full_calendar\n",
    "    )\n",
    "    monthly_coverage = transaction_flag.resample('M').mean()\n",
    "    \n",
    "    # 核心优化：大幅加宽画布，减少柱宽\n",
    "    plt.figure(figsize=(18, 7))  # 宽度增加到18，适配36个月数据\n",
    "    bar_width = 0.6  # 缩小柱宽，避免挤压\n",
    "    \n",
    "    # 配色：viridis色系，与系列统一\n",
    "    bar_color = sns.color_palette('viridis')[2]\n",
    "    \n",
    "    # 绘制柱状图\n",
    "    bars = plt.bar(\n",
    "        range(len(monthly_coverage)),  # 用索引定位，避免日期格式拥挤\n",
    "        monthly_coverage, \n",
    "        width=bar_width,\n",
    "        color=bar_color,\n",
    "        alpha=0.9,\n",
    "        edgecolor='white',\n",
    "        linewidth=1.2\n",
    "    )\n",
    "    \n",
    "    # 标题与标签\n",
    "    plt.title(\n",
    "        '月度交易覆盖率 (有交易的天数比例)',\n",
    "        fontsize=15,\n",
    "        fontweight='bold',\n",
    "        pad=20,\n",
    "        color='#333333'\n",
    "    )\n",
    "    plt.ylabel(\n",
    "        '交易天数比例',\n",
    "        fontsize=13,\n",
    "        fontweight='medium',\n",
    "        labelpad=15\n",
    "    )\n",
    "    plt.xlabel(\n",
    "        '年月',\n",
    "        fontsize=13,\n",
    "        fontweight='medium',\n",
    "        labelpad=15\n",
    "    )\n",
    "    \n",
    "    # 坐标轴优化（解决拥挤核心）\n",
    "    x_labels = [date.strftime('%Y-%m') for date in monthly_coverage.index]\n",
    "    # 仅显示每3个月的标签（减少50%标签数量）\n",
    "    tick_positions = range(0, len(x_labels), 3)\n",
    "    tick_labels = [x_labels[i] for i in tick_positions]\n",
    "    \n",
    "    plt.xticks(\n",
    "        tick_positions,\n",
    "        tick_labels,\n",
    "        rotation=45,\n",
    "        ha='right',\n",
    "        fontsize=10\n",
    "    )\n",
    "    plt.yticks(fontsize=10, color='#666666')\n",
    "    plt.ylim(0, 1.05)\n",
    "    \n",
    "    # 数据标签优化（只显示超过50%的标签，减少干扰）\n",
    "    for i, bar in enumerate(bars):\n",
    "        height = bar.get_height()\n",
    "        if height > 0.5:  # 仅显示高覆盖率标签，避免拥挤\n",
    "            plt.text(\n",
    "                bar.get_x() + bar.get_width()/2,\n",
    "                height + 0.02,\n",
    "                f'{height:.0%}',\n",
    "                ha='center',\n",
    "                va='bottom',\n",
    "                fontsize=9,\n",
    "                fontweight='bold',\n",
    "                color='#333333'\n",
    "            )\n",
    "    \n",
    "    # 网格线与边框\n",
    "    plt.grid(axis='y', linestyle='--', alpha=0.3, color='#bbbbbb')\n",
    "    sns.despine(top=True, right=True)\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    \n",
    "    return transaction_flag\n",
    "\n",
    "# 调用函数\n",
    "transaction_flag = analyze_data_coverage(df)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7fb01bc0",
   "metadata": {},
   "source": [
    "### 1.3 创建基于实际交易日的分析框架\n",
    "- return daily_metrics "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "78ffa3db",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "实际分析天数: 664天\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Order Date</th>\n",
       "      <th>Sales</th>\n",
       "      <th>Net_Profit</th>\n",
       "      <th>Quantity</th>\n",
       "      <th>Daily_Customers</th>\n",
       "      <th>Daily_Orders</th>\n",
       "      <th>Year</th>\n",
       "      <th>Month</th>\n",
       "      <th>Quarter</th>\n",
       "      <th>DayOfWeek</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-01-13</td>\n",
       "      <td>22163.23</td>\n",
       "      <td>6222.7607</td>\n",
       "      <td>164.0</td>\n",
       "      <td>51</td>\n",
       "      <td>53</td>\n",
       "      <td>2022</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-01-14</td>\n",
       "      <td>19190.46</td>\n",
       "      <td>5091.6834</td>\n",
       "      <td>145.0</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>2022</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-01-15</td>\n",
       "      <td>299647.08</td>\n",
       "      <td>86257.2678</td>\n",
       "      <td>1979.0</td>\n",
       "      <td>436</td>\n",
       "      <td>638</td>\n",
       "      <td>2022</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2022-01-16</td>\n",
       "      <td>21407.66</td>\n",
       "      <td>5838.9734</td>\n",
       "      <td>149.0</td>\n",
       "      <td>54</td>\n",
       "      <td>56</td>\n",
       "      <td>2022</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2022-01-17</td>\n",
       "      <td>26076.99</td>\n",
       "      <td>7921.1923</td>\n",
       "      <td>159.0</td>\n",
       "      <td>46</td>\n",
       "      <td>48</td>\n",
       "      <td>2022</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>659</th>\n",
       "      <td>2024-12-27</td>\n",
       "      <td>26774.65</td>\n",
       "      <td>7679.6069</td>\n",
       "      <td>184.0</td>\n",
       "      <td>52</td>\n",
       "      <td>55</td>\n",
       "      <td>2024</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>660</th>\n",
       "      <td>2024-12-28</td>\n",
       "      <td>19898.09</td>\n",
       "      <td>6041.6169</td>\n",
       "      <td>125.0</td>\n",
       "      <td>47</td>\n",
       "      <td>47</td>\n",
       "      <td>2024</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>661</th>\n",
       "      <td>2024-12-29</td>\n",
       "      <td>25822.37</td>\n",
       "      <td>7739.7581</td>\n",
       "      <td>163.0</td>\n",
       "      <td>51</td>\n",
       "      <td>52</td>\n",
       "      <td>2024</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>662</th>\n",
       "      <td>2024-12-30</td>\n",
       "      <td>24641.55</td>\n",
       "      <td>7082.0127</td>\n",
       "      <td>159.0</td>\n",
       "      <td>52</td>\n",
       "      <td>55</td>\n",
       "      <td>2024</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>663</th>\n",
       "      <td>2024-12-31</td>\n",
       "      <td>17947.12</td>\n",
       "      <td>4727.6150</td>\n",
       "      <td>122.0</td>\n",
       "      <td>44</td>\n",
       "      <td>45</td>\n",
       "      <td>2024</td>\n",
       "      <td>12</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>664 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Order Date      Sales  Net_Profit  Quantity  Daily_Customers  \\\n",
       "0   2022-01-13   22163.23   6222.7607     164.0               51   \n",
       "1   2022-01-14   19190.46   5091.6834     145.0               51   \n",
       "2   2022-01-15  299647.08  86257.2678    1979.0              436   \n",
       "3   2022-01-16   21407.66   5838.9734     149.0               54   \n",
       "4   2022-01-17   26076.99   7921.1923     159.0               46   \n",
       "..         ...        ...         ...       ...              ...   \n",
       "659 2024-12-27   26774.65   7679.6069     184.0               52   \n",
       "660 2024-12-28   19898.09   6041.6169     125.0               47   \n",
       "661 2024-12-29   25822.37   7739.7581     163.0               51   \n",
       "662 2024-12-30   24641.55   7082.0127     159.0               52   \n",
       "663 2024-12-31   17947.12   4727.6150     122.0               44   \n",
       "\n",
       "     Daily_Orders  Year  Month  Quarter  DayOfWeek  \n",
       "0              53  2022      1        1          3  \n",
       "1              51  2022      1        1          4  \n",
       "2             638  2022      1        1          5  \n",
       "3              56  2022      1        1          6  \n",
       "4              48  2022      1        1          0  \n",
       "..            ...   ...    ...      ...        ...  \n",
       "659            55  2024     12        4          4  \n",
       "660            47  2024     12        4          5  \n",
       "661            52  2024     12        4          6  \n",
       "662            55  2024     12        4          0  \n",
       "663            45  2024     12        4          1  \n",
       "\n",
       "[664 rows x 10 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 1.3 创建基于实际交易日的分析框架\n",
    "def create_daily_analysis_frame(df):\n",
    "    \"\"\"创建每日分析数据集\"\"\"\n",
    "    daily_metrics = df.groupby('Order Date').agg({\n",
    "        'Sales': 'sum',\n",
    "        'Net_Profit': 'sum',\n",
    "        'Quantity': 'sum',\n",
    "        'Customer ID': 'nunique',\n",
    "        'Order ID': 'nunique'\n",
    "    }).reset_index()\n",
    "    \n",
    "    daily_metrics = daily_metrics.rename(columns={\n",
    "        'Customer ID': 'Daily_Customers',\n",
    "        'Order ID': 'Daily_Orders'\n",
    "    })\n",
    "    \n",
    "    # 添加时间特征\n",
    "    daily_metrics['Year'] = daily_metrics['Order Date'].dt.year\n",
    "    daily_metrics['Month'] = daily_metrics['Order Date'].dt.month\n",
    "    daily_metrics['Quarter'] = daily_metrics['Order Date'].dt.quarter\n",
    "    daily_metrics['DayOfWeek'] = daily_metrics['Order Date'].dt.dayofweek\n",
    "    \n",
    "    print(f\"实际分析天数: {len(daily_metrics)}天\")\n",
    "    return daily_metrics\n",
    "\n",
    "daily_metrics = create_daily_analysis_frame(df)\n",
    "daily_metrics"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "babd2cbd",
   "metadata": {},
   "source": [
    "## 日均指标分析"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4eb373cb",
   "metadata": {},
   "source": [
    "### 2.1 计算关键日均指标"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "552fe680",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== 年度日均指标 ===\n",
      "      Avg_Daily_Sales  Std_Daily_Sales  Trading_Days  Avg_Daily_Profit  \\\n",
      "Year                                                                     \n",
      "2022         35047.22         59684.31           221          10085.07   \n",
      "2023         34946.97         57526.57           222          10091.23   \n",
      "2024         35221.16         57860.88           221          10188.71   \n",
      "\n",
      "      Avg_Daily_Customers  Avg_Daily_Orders  \n",
      "Year                                         \n",
      "2022                65.41             77.19  \n",
      "2023                65.26             76.73  \n",
      "2024                66.11             77.43  \n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Avg_Daily_Sales</th>\n",
       "      <th>Std_Daily_Sales</th>\n",
       "      <th>Trading_Days</th>\n",
       "      <th>Avg_Daily_Profit</th>\n",
       "      <th>Avg_Daily_Customers</th>\n",
       "      <th>Avg_Daily_Orders</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2022</th>\n",
       "      <td>35047.22</td>\n",
       "      <td>59684.31</td>\n",
       "      <td>221</td>\n",
       "      <td>10085.07</td>\n",
       "      <td>65.41</td>\n",
       "      <td>77.19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2023</th>\n",
       "      <td>34946.97</td>\n",
       "      <td>57526.57</td>\n",
       "      <td>222</td>\n",
       "      <td>10091.23</td>\n",
       "      <td>65.26</td>\n",
       "      <td>76.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2024</th>\n",
       "      <td>35221.16</td>\n",
       "      <td>57860.88</td>\n",
       "      <td>221</td>\n",
       "      <td>10188.71</td>\n",
       "      <td>66.11</td>\n",
       "      <td>77.43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Avg_Daily_Sales  Std_Daily_Sales  Trading_Days  Avg_Daily_Profit  \\\n",
       "Year                                                                     \n",
       "2022         35047.22         59684.31           221          10085.07   \n",
       "2023         34946.97         57526.57           222          10091.23   \n",
       "2024         35221.16         57860.88           221          10188.71   \n",
       "\n",
       "      Avg_Daily_Customers  Avg_Daily_Orders  \n",
       "Year                                         \n",
       "2022                65.41             77.19  \n",
       "2023                65.26             76.73  \n",
       "2024                66.11             77.43  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2.1 计算关键日均指标\n",
    "def calculate_daily_metrics(daily_metrics):\n",
    "    \"\"\"计算关键日均指标\"\"\"\n",
    "    # 年度日均指标\n",
    "    annual_daily_avg = daily_metrics.groupby('Year').agg({\n",
    "        'Sales': ['mean', 'std', 'count'],\n",
    "        'Net_Profit': 'mean',\n",
    "        'Daily_Customers': 'mean',\n",
    "        'Daily_Orders': 'mean'\n",
    "    }).round(2)\n",
    "    \n",
    "    annual_daily_avg.columns = ['Avg_Daily_Sales', 'Std_Daily_Sales', 'Trading_Days', \n",
    "                               'Avg_Daily_Profit', 'Avg_Daily_Customers', 'Avg_Daily_Orders']\n",
    "    \n",
    "    print(\"=== 年度日均指标 ===\")\n",
    "    print(annual_daily_avg)\n",
    "    \n",
    "    return annual_daily_avg\n",
    "annual_daily_avg = calculate_daily_metrics(daily_metrics=daily_metrics)\n",
    "annual_daily_avg"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "79d77274",
   "metadata": {},
   "source": [
    "### 2.2 年度日均值比较分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "28084b8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x1200 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 设置中文字体，统一全系列图表风格\n",
    "plt.rcParams[\"font.family\"] = [\"SimHei\",]\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "def plot_annual_comparison(annual_daily_avg):\n",
    "    \"\"\"绘制年度日均值比较图（优化视觉设计）\"\"\"\n",
    "    # 统一配色：使用viridis色系，与前期图表保持一致\n",
    "    palette = sns.color_palette('viridis', n_colors=len(annual_daily_avg.index))\n",
    "    \n",
    "    # 保持2x2布局，调整画布尺寸更协调\n",
    "    fig, axes = plt.subplots(2, 2, figsize=(16, 12))\n",
    "    axes = axes.flatten()  # 扁平化轴数组，方便循环处理\n",
    "    \n",
    "    # 定义子图信息（标题、y轴标签、数据列名）\n",
    "    plot_info = [\n",
    "        ('日均销售额比较', '销售额 ($)', 'Avg_Daily_Sales'),\n",
    "        ('日均利润比较', '利润 ($)', 'Avg_Daily_Profit'),\n",
    "        ('日均客户数比较', '客户数', 'Avg_Daily_Customers'),\n",
    "        ('日均订单数比较', '订单数', 'Avg_Daily_Orders')\n",
    "    ]\n",
    "    \n",
    "    # 循环绘制4个子图\n",
    "    for i, (title, ylabel, col) in enumerate(plot_info):\n",
    "        ax = axes[i]\n",
    "        # 绘制柱状图（统一风格：白色边框、viridis配色）\n",
    "        bars = ax.bar(\n",
    "            annual_daily_avg.index, \n",
    "            annual_daily_avg[col],\n",
    "            color=palette,\n",
    "            edgecolor='white',  # 白色边框增强区分度\n",
    "            linewidth=1.2,\n",
    "            alpha=0.9\n",
    "        )\n",
    "        \n",
    "        # 添加数据标签（与系列图表标注风格一致）\n",
    "        max_val = annual_daily_avg[col].max()\n",
    "        for bar in bars:\n",
    "            height = bar.get_height()\n",
    "            ax.text(\n",
    "                bar.get_x() + bar.get_width()/2,\n",
    "                height + max_val * 0.02,  # 标签位置自适应最大值\n",
    "                f'{height:.0f}',\n",
    "                ha='center',\n",
    "                va='bottom',\n",
    "                fontsize=9,\n",
    "                fontweight='bold',\n",
    "                color='#333333'\n",
    "            )\n",
    "        \n",
    "        # 标题与标签优化\n",
    "        ax.set_title(\n",
    "            title,\n",
    "            fontsize=14,\n",
    "            fontweight='bold',\n",
    "            pad=15,\n",
    "            color='#333333'\n",
    "        )\n",
    "        ax.set_ylabel(\n",
    "            ylabel,\n",
    "            fontsize=12,\n",
    "            fontweight='medium',\n",
    "            labelpad=10\n",
    "        )\n",
    "        # x轴刻度优化\n",
    "        ax.set_xticks(annual_daily_avg.index)\n",
    "        ax.tick_params(axis='x', labelsize=10)\n",
    "        ax.tick_params(axis='y', labelsize=10, color='#666666')\n",
    "        \n",
    "        # 网格线（仅y轴，统一风格）\n",
    "        ax.grid(axis='y', linestyle='--', alpha=0.3, color='#bbbbbb')\n",
    "        \n",
    "        # 去除顶部和右侧边框\n",
    "        sns.despine(ax=ax, top=True, right=True)\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "# 调用函数\n",
    "plot_annual_comparison(annual_daily_avg=annual_daily_avg)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "53b357d7",
   "metadata": {},
   "source": [
    "### 2.3 基于实际交易日的趋势分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "be899f73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/Fm2JiIiIMsyJJ54Iu90Og8EARVHg8/nQv3//kL8Jftzn80FRFIwfPx6LFi1q8n1HjRqFCy+8EJMmTWoxBoPBgKFDh2Lq1KkYMGAA5s2bhwULFgAAKisrIwpsTdHr9Rg9ejRef/113HXXXXjppZewfv16vPDCC9rfvPPOO1i8eDHeeuutkMLtjBkzcPHFF+P000/XZot+/vnnOOWUUwCoX8ZWVVWFkSNHol+/fjjnnHOi3nt29erVeOSRR2C32zFr1iw8/PDD0Ov10Ol0WtsZDAb4fD4MGDAAK1eujHgPnU6HadOm4brrrmtyXa+++uomn7vmmmtCfu/VqxcOO+wwAMC+ffvw3HPP4dFHHwUQOtMWUG+R4Ha74XK5UF9fj4MHD0Kv16N79+5aMXnZsmXa3zc2Nmox+/8fOXIk3G43ALT4RWRutxsOh0O7d7FOp8Ptt98OQJ0xazQa0a1bN7z44otwOp247bbbtPfyeDwRRXO/+vp6rFy5EhdccEHEc3a7HU8//TTMZjP0ej30ej2sVmvIegDq/X7POeccmEwmbTbt77//jrvuuguXXXYZAGi3wbj99tsxdepUFBQUxJyvREREROnCoi0RERFRhlm+fDk6dOiADh064OOPP8bdd9+NVatW4fDDD9f+5sMPP8RDDz2EN954A506dUJVVVXKLv+22WzQ6XT485//jPPPPx+vvfYaiouL4fF4AAB79+5F9+7dtd/991n1+XzavVaDtW/fHpWVlVAUBW+++Sb69u0bMpO0d+/e2Lp1K95++21ccskl2uOlpaU4/fTTAahfunbKKadg7ty5WLVqFSoqKvD444/j9ttvR79+/QBEflnYxx9/jEWLFqGmpgYLFizAKaecglWrVuFvf/sbVq1ahZKSEpx//vm44IILcPvtt2PGjBlN3v5AUZSY2q6pv1uwYIH2JWrvv/9+SJH12muvxT333IM777wT3bt3j7j364IFC/Cvf/0LDocDW7ZswQsvvIA//OEPWLp0KQDgtNNOC/miuurqam2WbDCv1wtAncEcnivz5s3DvHnzAARmXgPAli1bAKi3y3j++eexd+9eLF68GEOGDMGGDRvw4IMPhtwqw2KxaPeVDXfgwAHMnTsXo0aNAgAsWrQIixcvxuOPP47jjz8eJpMJBoNBK6hHmy3epUsXvPzyyzjmmGO0+/V26dIFL774ola09fvLX/6Cfv36IS8vL+S2G0REREQyYNGWiIiIKMP4C3BffPEF7rvvPpSVleHwww/Hhg0bsHLlSvztb3/DqFGj8NNPP+GGG27ASy+9hB49eqRs+WVlZXjrrbdCHnv55ZdDfp86dSqmTp0a8lifPn1C7m1bX1+Pe++9F59++ilGjBiBr7/+Gjt27MCTTz4Z8rpjjjkGI0aMwKJFi3DRRRdFnS3rj+uiiy7CQw89hG+//RYnnngi/vKXvzS5Ht27d8fZZ5+Nq6++GiaTCc8//zyWLl2KhQsXolOnTnj00Ufh8/lw4403AgAqKiqavO1DrF9S1lTRtrS0VCtmhhe2R44cicLCQrz33nv4y1/+AofDod3+AABmz56N2bNn4+qrr8bZZ5+tzYj1O+aYYyJuj9BUbH/5y19w7rnn4tBDD212PXQ6nVbkdbvdePbZZ3HgwAHU19fjjjvu0P7uoosuwi233KLd5mDLli0YOHCg9nxwu1VWVgKAdr/cCRMmaH1jMpkibtuwf/9+vPjiiyFtevnll2Pz5s1RYw4uXPs9//zzGDJkSLPrSkRERCQCi7ZEREREGaahoQELFy7EihUrMHXqVJx++ulwOp3Yu3cv/vOf/8DpdMLr9eLPf/4zjEYjLr74YkycOBFjx46NOtM1Xvfeey+mTJkScl9dQC3ATZo0Cfn5+XjkkUe0S/h9Ph+cTmfE+xQXF2Pnzp24+OKLMWnSJEyYMCFiVqjfTTfdhCuuuALvvPMOxo4dGzWubt264corr8Rzzz2HwsJC/OMf/2h2dnG/fv3Qu3dvvPfee3jxxRfRpUsX/OMf/0CPHj3w+uuv4+9//zteffVV5OfnA1CLncOGDYurrcLFOiM3mNls1mbPAurtDfyzSKOxWCwoKSnRfr/hhhtC2qGpGLZt24YXXngh5NYUTbnhhhtw7733AlDvm7t06VK88cYb+PTTT/H8888DUG8/YDQaodfrYTKZ4Ha7sX79eixduhQ+n0+7/YPf1q1bAQCffvopALUw7G/7AwcOoLGxMWSm7YEDBwCEFn5ffvll6HQ6mEwm6HQ67Nu3D5deeinuuOMOXHrppdrfVVRU4KKLLmpy1i8RERGRaCzaEhEREWWQ+vp6nHvuuejfvz9WrFiBzz77LGKm4DHHHAMAaNeuHb766iscc8wxmDlzJqqqqnDPPfc0+d6xFhRLS0sjHrNYLJg+fTp27NiBpUuXYuLEiSgpKcHdd9+t3Z4gmH+W5uLFiwGos4a//vprvPrqq1GXOXjwYBxzzDFYsWJF1KLtnj178Pzzz2PFihW4+OKL8dlnn+Hyyy/HLbfcgvPOOy/kHrDB67tw4UL8/PPPuPfee3Haaadp97VdsWJFyC0LvF4v9uzZ0+SMZa/Xizlz5mDOnDlNtJqqqS+7qqmp0W69UF9fH/G8v08Bta2bKtpu27YN48aNw8MPP4yTTjoJAPDiiy+GzLStqKgIuWetn7+g/+9//7vZYubFF18c0Z7RblUABG5JsWjRIjz00EMYNWoU7HY7zj33XDzxxBPo06eP9rfffPMNTjjhBG3WbnDMzz77LFauXAmDwQCdTge9Xg9A/RI2/60agNDc3Lt3L6ZNm4auXbviyiuvDLmlRHV1NQCkdAY6ERERUSqxaEtERESUQYqLi7F69Wo4HA4oioLTTz8dp512GkwmEz788EMsWbIEr7/+OjweD1wuF/bt24fDDjsMb7zxRtT7diqKgnvuuQcNDQ3YvXt3yGX3sdi6dSvWrl2L5cuXo0uXLnjttdfQu3dv3H333Xj22WcxduxYXHDBBbjjjjvQvXt37XXBhTYA6NmzJ26//XaceOKJ+Pe//41169Zhy5YtIV8Q9dhjj2nFxIaGBvz666/45Zdf8O9//xtff/01Bg4ciGXLluG4445DbW0tnnzySUyfPh0PP/wwhgwZgkGDBuHQQw/FueeeqxX/pkyZAgDYsWMHnn32WSxbtgx/+MMfsGLFChxxxBHYvHkzdu7ciXXr1sHj8eCoo46K2g5erxeTJk3CuHHjmmyrO+64QytWB/P5fLjzzjtDHgtuq2AWiwWVlZURM6bdbjcqKirw7bffYuLEiRg6dKj23sGcTie2bdsGABGzkP2F18bGRthstibXQ1EUrWgavh7r16/Xbn/g8Xhw6KGHwul0Yt68ediyZQtefvll+Hw+DBo0CH/605/w5JNPYvjw4di7dy8+/fRTPPnkkzjhhBPwzDPPYOXKlaisrET//v1x9tlnY/z48Vrh1ePxwOl0wmazYc+ePTjiiCO02DZv3oy1a9fi//7v/3DooYdiyZIlMJvNcDqdOHjwILxeL15//XX07t272RnLRERERCKxaEtERESUYQ455BBceOGF2LlzZ9Qi60UXXaT97HK54Ha7MXv2bFx44YURf6vT6aAoCjZt2oQTTzwR55xzTovLr6mpwZw5c/DDDz9g586d6NmzJ2677TZcfvnlWlHthBNOwAknnIB169ahrKwM55xzDmbPno0LLrgAQPSi7a233gpAnT35ySef4Pjjjw9Zl969e2s/79u3D9dccw1ycnJw+umn44UXXgi5dUH79u0xY8YM3HLLLXjnnXfw/vvvY+HChRg5ciTOP//8kGW///77mDlzJkaOHIlnnnkGgwcPDmm/hx56CEceeSTmzJkTcUm/n9frRWFhYbMzVM1mc0TR1uVyQVEUvPbaa9rMUv+XoQWbP38+Kisr8d1336FLly4YNGhQyPM+nw9HHXUU5s6dG1LoDr8txQsvvIBnnnkGJ510Ejp16hTynL9PYskB/xfABXM6nTj11FNDbo9QXV2NMWPGoE+fPliyZIn2wcH8+fPx4IMPYuvWrVrR9rDDDsOpp54Ko9GIv/71r7jpppvw4Ycf4j//+Q/Ky8uxb98+WCyWiDa86qqrcOqpp2Lv3r0YP348qqqqMGjQIMyaNQvnnHOOVmDW6XS4/vrrsWPHDhQWFuLhhx9ucT2JiIiIRNEpidxYi4iIiIiy2vLly7Fnzx6ceeaZGDRoULP3jrXb7Xj11Vdx1VVXpeSeun7fffcd+vfvH/XWB9HYbDa4XC60b98+4jlFUZpdh9bi9Xqxdu1anHzyyejYsSMA9dL93bt3hxRmX3nlFXz77bcYPHgwLr744oTb0W63w+PxRH39jz/+iEsvvTSm2yOceOKJ2j1tW3Lw4EFt3Zpjs9lQUFDQ4t+5XC5tBrHRaAz5Yrqvv/4aXbt2Rc+ePaO+dvv27dDr9ejZs2fU2cJEREREsmDRloiIiIiI4PF4UF9fj3bt2rGgSURERCQYi7ZEREREREREREREEuFH6EREREREREREREQSYdGWiIiIiIiIiIiISCIs2hIRERERERERERFJhEVbIiIiIiIiIiIiIokYRQeQ6Xw+HzweD/R6PXQ6nehwiIiIiIiIiIiISFKKosDn88FoNEKvb3o+LYu2SfJ4PNi0aZPoMIiIiIiIiIiIiChDDBw4EGazucnnWbRNkr8iPnDgQBgMBsHRxE5RFNTX16O4uJgzhLMY84CawtygcMwJ8mMuUDTMCwrHnKBgzAeKhnlB4bIlJ7xeLzZt2tTsLFuARduk+ZPIYDBkXNFWr9fDYDC06Q2Bmsc8oKYwNygcc4L8mAsUDfOCwjEnKBjzgaJhXlC4bMuJltaRX0RGREREREREREREJBEWbYmIiIiIiIiIiIgkwqItERERERERERERkURYtCUiIiIiIiIiIiKSCIu2RERERERERERERBJh0ZaIiIiIiIiIiIhIIkKLtrW1tfj2229RU1MjMgwiIiIiIiIiIiIiaQgr2r777rs466yz8PDDD2PEiBF49913AQBbt27FJZdcgqFDh2LevHlQFEV7TbqfIyIiIiIiIiIiIko3IUXb+vp6lJWVYdmyZVi9ejVmzpyJxx57DC6XCxMmTMCAAQOwcuVKlJeXY9WqVQCQ9ueIiIiIiIiIiIiIRBBStLXZbLjvvvtw1FFHAQD69esHi8WC9evXw2q1Ytq0aejVqxcmT56MFStWAEDanyMiIiIiIiIiIiISwShioV27dsWFF14IAHC73XjppZdw1llnYfPmzRg0aBDy8vIAAH379kV5eTkApP25eCmKklG3VvDHm0jMjY2NMJlMMBqFpA+lUDJ5QG0bc4PCMSfIj7lA0TAvKBxzgoIxHyga5gWFy5aciHX9hFbdNm/ejGuuuQYmkwn//Oc/sXDhQvTo0UN7XqfTQa/Xw2KxwGq1pvW5kpKSuNalvr4eer3Q73WLi6IosNvtANT1jpXL5cLu3buRl5eHrl27tlZ4lCaJ5gG1fcwNCsecID/mAkXDvKBwzIm2wel0wmazoV27dkmd7zIfKBrmBYXLlpzw+Xwx/Z3Qom3fvn3xyiuvYN68eZg2bRoOPfRQmM3mkL/JycmBw+GAwWBI63PxFm2Li4thMBjieo1I/qp+SUlJXBuC3W5Hfn4+zGZz3G1E8kk0D6jtY25QOOYE+TEXKBrmBYVjTrQNe/bsgcvlgtFoRFFRUcLvw3ygaJgXFC5bcsLr9cb0d0KLtjqdDv3798fcuXMxYsQITJ48Gdu2bQv5G5vNBpPJhJKSkrQ+l8i6ZFpC+WOOJ+7g12Ta+lJ07M+2w2KxwOfzoX379il5P+YGhWNOkB9zgaJhXlA45kTboNPpoChK0v3IfKBomBcULhtyItZ1E3I9/5dffol58+Zpv/tnqB5++OH44YcftMd3794Nl8uFkpISDBw4MK3PUXRt/b4iRJmssrISVVVVMX9qR0RERERERERyElK0Pfzww/H666/j9ddfx759+/D444/j5JNPxumnn46GhgasXr0aALB48WIMGzYMBoMBQ4cOTetzRESZJBtu1k5ERERERESULXSKoLP8zz77DHPmzMH+/ftxyimnYMaMGSgtLcVHH32EKVOmoKCgAF6vF0uXLkWfPn0AIO3PxcLr9eL777/Hsccem1HFXkVRtC9ci2fKuc1mw+7du2E2m3HYYYe1YoSUDonmAclHURRs3boVAHDEEUfAaEzu7jfMjbYjFZcz+t+HOUEAc4GiY15QOOZE21BRUQG73Y4uXbokdUUq84GiYV5QuGzJiVhricLuaTt8+HAMHz484vGRI0figw8+wKZNmzB48GCUlpYKe46IiDKPw+HA3r17ccghhyT1hRltwd69e+FwOHDooYcm9Y3PRERERERElF5Cv4isKZ07d0bnzp2leI4oXENDA5xOJzp27Cg6FCKKwm63w+12w2azZX3R1m63w+v1wuPxwGw2iw6HiIiIiIiIYsRpN5SQbL535oEDB1BdXQ2n0yk6FCKKIpvHp6awTYiIiCgZPJYgP5vNhl27dsHlcokOhajNY9GWKE7+AxYeuBCR7DhOEREREVEq1dfXo7GxETabTXQoRG0ei7YUFxYAiOTEbTMS24SIiIiIqHXwWJuo9bFoS0RERERERCQZGYpiMsRARJStWLQlihMPXEh2zFEiIiKizGa1WvHbb7+hoaFBdChEUqqtrUVFRQV8Pp/oUIhaDYu2RERE1OZ5PB6Ul5fj4MGDokMhIiJqUWNjI3w+HxobG0WHQhRClgkiFosFdrsdDodDdChErYZFW0qILAM1EaAWY4j8OD5RNA6HAx6Ph1+aQURElAAeX5GsmJvUlrFoS5Qg7hzkYLFYUF5eDovFIjoUIun4xymOV0RERESUSjy+lAf7ou1i0ZaIEma32+H1eoXG4HQ6AQAul0toHKJxRx2JbUJERERE1DbxWF/ldrtRXl6Oqqoq0aFQK2DRlogSYrfbUVFRIc3OgTttIiKizOR0OrP+w1dZOBwO1NTU8LhKAuwDIoqF0+mE1+vl/a/bKBZtKS48eCA//31kRc+0pUjcTkk2NTU1wm9hwu2CZOLxeFBfX8+8hLpt7tq1C7t27WJ7SODAgQM4cOAAT/4lInq7EL18ClAUBY2NjcL7RPTyKTr2S9tkFB0AUabhYBiK7UFEzfF4PDhw4AD0ej1KSkpEh8Mxi6Rw8OBBWCwW6HQ6FBUViQ5HKJ/PB5/PB0DdPnU6neCIspu/L/z/E5E8GhoasG/fPpSWluKQQw4RHQ4RpQFn2hJRQlj4IKJY8MvQiCL5r1Lh1SpERBQr/5WO/v9JxWNMastYtKWEiB4YnU4nduzYAavVKjQOIpKP6PHJT6bLn2WIgYhIZhwniYiISDYs2lJGstvtcDqdaGhoEBYDD+6J5CZ6G62ursa+ffs4ThERSc7lcqG8vBwHDx4UHYpw3G9QU5gbFE50TohePlE6sGhLRBmNO2uVLO2gKAr279+Puro60aEIx8ufiYgyg/+bt+12u+hQiIiIiDQs2lJcZCoMBf9PxFyQg8vlgsViQU1NjehQSBKyjNeil08Ujei8bGhoQFVVlfA4iCgS958Ujn1B0TAv2jYWbYnixEExFNtDnaFjsVhEh6ER2SeynGAQEclMljHy4MGDqK2thdPpFB0KAHHtIkt/APLEIkscRBRJ9PYpevkUHfulbWLRlogoSZWVldi/fz8cDofoUEhCLELIhe1CFCDDB23cJgNsNht+++03ofdCJyIiInmwaEtECeFJVoDP5wPAe5fKgrkpL/YNEVHTGhsb4fP50NjYKDoUIqKMweNLastYtCVKEHcORNQcmcYImWIhIqLmceYzyYi5QbJhTlI2YNGWEiJ6gJThcj6SA3NBLjL1hwwxEBHJjONkKNHtIXr5RETx4JhF1PpYtCUiSpIMBywyxEAUDXOTqGmybB+yxEHyYE6Ixz4IqKqqwt69e0WHIZxMkyNIHsyHto1FW4oLBwS2QTi2BxFlAo5V5OdyubBjxw7U19eLDoXCcDsliiR6uxC9fACoq6tDQ0MDPB6P6FCIpCXDtkqpx6ItEVEbwx22XNgfRHKx2+1wOp1oaGgQFgPHBbmwP4jkxm1ULrL1h2zxEKUSi7ZElBDuHCOxTUgmzMfosrldFEWBz+cTHQZRiGzeJokosxw4cIBXaRA1weVyiQ6hTWLRloiI2hRZCgCyxAHIE4sscWSriooK/P777yzcEkmOYyVRgCzbg8vlQk1NDQ4cOCA6FCLp1NbWYvv27bBYLKJDaXNYtKWEyLLzTLfg9c7WNpAN+0FO7Bci+TidTni93qy/JyDHpwC2RSjR7SF6+bLEQHKSITdExsAvAYskui1EL58C3G43AM62bQ0s2lJG4k6TwslwECeSDDEA8sQByBULEVE0soxTssRBRESUSaqrq1FXVyc0Bu7D2zaj6ACIKLNxJ0FEzZFljJAlDiKA+UjyY46Kxz6Qiwz9wYlLcvF6vTh48CD0ej3atWsnOhzmRRvFmbYUFw4ERPLjdioX9gdRgMPhkOb2DNw25cBbTxE1T/R2IXr5JB/mhIpF9AC2Qeth0ZaIEsKBORLbRA7sBxXbIbpsbhfR6+52u7Fz507s3btXaBxE4URvG0RElDjRY7jo5VPbxqItERERpQUPauUgqh/8M2xlmWlLRERElCge11I6sGhLREQpx4MYudpApliIROP2EMC2CCW6PWS41FZ0GxCFk+0WKjLEQCQrbh+px6ItJUT0xijqoFa2gwaSox9kiEEWMrWFTLFkM/aDiu1A0ciSF7LEQUTyyvZxItvXPxrRbSJ6+cFkioXaFhZtiSgpsuygZIlDlGxff6KWiNxGqqurUV5eDrfbLSwGokzAfZk82BdE8pFhNj7JR4Z8kCGGtopFW4oLN0YiIqL42Gw2eL1eOBwO0aEIJdvJJi9BJz/2BzWlsrISdXV1QpYty5gpevlEFBtuq20Ti7ZElBDuFCLJ0iayxEEq9gfJgrlIMmJekqxcLhfq6upQXV0tOhQiqcg2bouKR7Z2oLaJRVtKGAcpIpKRLGOT6DhELz8aGWKSIQbR2AZERC2TZaZrtpOt/WWLh4jaNmFF248++ghnnnkm+vfvj0svvRTl5eUAgLKyMvTt21f7N2rUKO01W7duxSWXXIKhQ4di3rx5IQNmazxHRPLjNisn9gtRJG4XcmA/BLAt5CJDkZA5QTJjfhLJjdto6gkp2u7atQv33XcfpkyZgvXr16Nbt264//77AQA///wzFi9ejI0bN2Ljxo146623AKiXp0yYMAEDBgzAypUrUV5ejlWrVrXacyQ3UQe1wcvjgCQXnuDIgW0hJ/YLEVHLOFbKw+12w+VyiQ6DgmT79iHT+ssSi+g4RC8/mEyxUNsipGhbXl6Ou+66C+eddx46duyIK664Aj/99BM8Hg+2bt2KIUOGoLi4GMXFxSgsLAQArF+/HlarFdOmTUOvXr0wefJkrFixotWeIyKizMaDJzmwH+RoA8YgJ1naRJY4SB6ic6KiogI7d+6Ez+cTGgfA+2WSPJgTFA3zom0ziljoiBEjQn7fvn07evfujS1btkBRFIwZMwaVlZUYOnQoysrK0K1bN2zevBmDBg1CXl4eAKBv377aLRVa47l4KYqSURuLP954Yw5+jeiZjYmuQyqWG/5zpkqmDf2v8/l8WZkLssUQ3A+piCOTxwgZ+iM4DlHbSKrHq1TlhOhtREQMMuRkKvsh2VyQZb8hMg6R+RgslbmZijFC9HjZlvpDhhiSeR+32w1FUeDxeGAymZKKI1Gi+0OGsSo4DtH7jvCf043HuAGp3Iel4nxU9DYa/rOoONrKcY0MOZ4Osa6fkKJtMJfLhZdeegnXXXcdysvL0adPHzzwwANo3749Zs2ahQcffBBLliyB1WpFjx49tNfpdDro9XpYLJZWea6kpCSu9aivr4denznf66YoCux2OwB1vWNVX1+vvc5iscT12lRqaGiA3W6H1+uFxWJJ23K9Xq+2/g0NDRk/kCSaB0CgD4xGY1r7IJzVag3JSRFsNht8Ph/q6+uF5YTT6QxpB7fbndT7JZobNptNe11dXZ2QMcKfE+keH6LF4XQ6YTKZkJOTk/blezwerS/q6+uTnq2UaE40NjaG5KbRKObQwz9m+ffz6WSz2eB0OoWOEcH9Z7FY4HQ6U/Je8eSC3W6H3W6HwWAQum36c8Hn8wndb7hcLpjNZpjNZiEx+OMAxI4Rwfuv+vp6Ie3hP76V5ZhGp9MJP6ZpaGhI+twmmePM4NwUtd/w56aI/QYgxzEuoPaF1+tN+ngm0XwIPv+yWCxCjqmAyOMZEfw5IXL/BQT2YanYn6dqnBBxzuFyuUJyQlQ9SOQxrp8/Nw0GA3Jzc5N6r2RyIpPEetwlvGi7YMEC5Ofn47LLLoPJZMKFF16oPTd9+nSMHDkSVqsVBoMh4iAuJycHDoejVZ6Lt2hbXFwMg8EQ12tE8p80lpSUxL3TdDgcCb02lZxOJzweD/Ly8uLuq2R4vV7k5+cDAIqKilBcXJy2ZbeGRPMAUGdA+AtS6eyDcFarFQBQWFgoLI6CggJ4vV4UFRUJi8HhcKC2thaAOh4VFBQk9X6J5obBYEBDQ0NCr00V/8Fsbm6u0Ny0WCwwGAzCctPj8WjjVXFxMYqKipJ6v0RzwmQyaQeQJSUlwk6+/fsvvV6f9v6oq6uDwWDQbv0kgqIoIfngv8oo0fcC4s8Fo9GI+vp6GAwGodumx+OB0+lM+zFEsNraWhiNRqH7LkDdfymKInSMCN9/iSjI+McH0cc0jY2N8Hq9beaYJpnjzODxStRMW39uithvAIFj3IKCAqF5mZ+fn5K8TDQfgo9nSkpKhBVt/cczOp1O6PG+iHPgcKnchyUzTvj3YaLOwZxOZ0huiira+nw+NDY2ChurgMD5l+icyCRerzemvxNatN2wYQOWL1+ON954I+rOuLi4GD6fD1VVVSgpKcG2bdtCnrfZbNrBVaqfi5dOp8u4hPLHHE/c4X8vap2DY09nDMHLy8Q+jybRdhTVBzLGIVMM4T+n4j0THSNkaA/R26joOGTLieCY0k3kdhq+XYiSynxIJhe4bbbdtsjU/YYs22jw8ttKbiZznBn8ehFEb6eil98acWTqGBEtDpExtKWcCH+/eF8X/Pp0ky0321IMotcnHWJdN2HX81dUVODuu+/GjBkzcOSRRwIA5syZg/fee0/7m02bNkGv16Nr164YOHAgfvjhB+253bt3w+VyoaSkpFWeI7mJvp8QyUOGHJAhBqLmiL4XXrZjG1BTZMkNWeIg8ZgLkdgm8uDxjFxEt4no5QeTKRZqW4QUbR0OB26++WaMHDkSZ555Jmw2G2w2G/r164cFCxZg48aN+PLLL1FWVoaxY8ciLy8PQ4cORUNDA1avXg0AWLx4MYYNGwaDwdAqzxFFEzwYc2CWC/tDDjL1g0yxEIkmw/YgQwxEJD8ebxNFx4lLFA3zoW0TcnuEzz//HOXl5SgvL8cbb7yhPf7xxx/j999/x8SJE1FQUICRI0di8uTJaqBGI8rKyjBlyhTMnz8fXq8XS5cubbXniCg23EnIgf1A1DxuI/KQpS9kiYMCRH5RH4Vim4hvA9HLDydbPCIpitKmL9umzMSZ6G2TkKLtyJEjsWXLlqjPTZkyBVOmTGnydR988AE2bdqEwYMHo7S0tFWfo0jcGOXBgwX5cPugaJgXJBvmpHgy9IEMMQDyxEEkK24jJBvmpIrtQOkg9IvIEtG5c2d07tw5bc8RyUhRFOzcuRMGgwE9e/YUHQ5JRpYDCH6wQDKSZfsgIiLKBKL3m6KXT3LibVTkxL5IvYwr2pI8uEGK4/V64XQ6AYgrjLH/SVay5KboOEQvPxoZY0qnbF9/WbAfSHbMUaLouG0QUbYR8kVkRMniTdhJJszDALYFUSRuFyQ75qhKdDuIXr4sMciGbUKAXHkgUyykYp9Qa2HRlogyGgv4RC3jFxOQDP3A8ToS24JkJct+g9sIUYBs24Ns8WQr9kPbxqItURx4EBmJ7UCyYm5SMOZDANtCxXaQD/uEZMJ8DMX2IAol2zYhy4dtlFos2hIRpQh3lKFkjEkEtgMRhZNhXJAhBlmwLaglInKEVyfIi30iF/YHtWUs2lJcOCCSH3OBiCg+HDflwH6QC/uDSG6it1HRy6dI7BNqCnMj9Vi0JcpAvE0DNSfbc0KW9Rcdh+jlRyNDTDLEQEQUDccnouZxGyE/5gJlCxZtKWEyXArOwZpkyAEZYiAieXGMIMoMsmyrMhxjiyRDDEThZMpLmWIhlSx9IksclDos2hJRm8AdlBzYDxSOOSEHGfqBH7hGkqUtZImDKFy2F7CJZMZtRA7sh7aNRVuiOPC2BESUiTheUTDmgyrb20HG9ZcxpmzFviBqmqjtg9ulXNgfAWyL1sOiLRElhQN0AGeDyEWWNpEljmzHD91IJsxBoswiYpuVbZyQLR4ikg/HidRj0ZYoA7H4IA+2f9PYNkRE1BIZ9hUyxCADtoOcsr1fsn39ZSRbn8gWD1EqsWhLRAnhzlFe7BsiufBerpF4ZQJR05ijJBOZ8lGmWEgs2XJBtnio7WDRluLCwYiIZCfLOCU6DtHLj0bGmIiI26af6HYQvXxZyNIOssRBcmA+EJEILNpSwmSYJcOdJzEHiOTGbVQOMvSDDDHIhm1C1DxuI0QBPAeWi4z9IGNMlBwWbYkoKbLsGGSJI9uxH+TEfqFgzAeSFXOTZMXcpGDMBxXbQQ4y9IMMMbRVLNoSxUGWLwDjoCgn9otc2B8UjPlA4bI9J7J9/YOxLSKxTQhgHhBRfDhmpB6LtkQZjgOjWGz/prFtVGwH+YjqE+aCHGToBxlikAXbgig62S6F576TZMUcobaMRVsiSgh3jnJhf8iHfUJ+zIVIbBMi+XC7JNkxR+XAfojENqHWwqItxUWWwUi2T55JHOZApGxvk2xffz8Z20HGmIiI26af6Hbg8a1KlvWXJQ6Sg0z5IFMsorANomO7tD0s2lLCOCCQTJiPcmA/UDjmhBxkuCc7c4GI4sVxg4hkxfGJ0oFFWyIiIiLKOjKcbMkQg0zYHvJgX4Rie1AwfvhJMpEhL2SIoa1i0ZYoQSIHJpkGRZliEY1tQbJibsox05RIJtwOiOTGbZRkxxylcMyJ1GPRligOMg5CMsaUTdj+TRPdNqKX7ydLHCQecyEg2z/4lCEGCuCHOqpsXneKToZtg3lJLWGOUFvGoi0RERFRK+LJBFHTuH1EyvY2kWX9GQdRdMzJSGwTai0s2hJRQrhjkgv7Qz7sk0hsk+zG/pcX+0YO7IdIbBMKlu1XaVAA+4OyBYu2FBdZBkd/HLLEk26yrTcPoOSS7W2S7evvx3agYDJc4kqR2BdERBQv7jvkIGM/yBgTJYdFW0oYBwSSCfNRDuwHCsecICKiTMd9GQXj/X1VssWTrWToBxliaKtYtCWKg4yDkYwxEZFcOE6wDShStueEjOsvY0zZin1BRETx4r4j9Vi0JUoQByQCeMkxtYx5IZ9090m239Inmmy/rY4MMVAA+4NkJENeMgbKBDLkiAwxUNvEoi0REbUK0QcvopdPREQtk22sli2edJJl3RlHKFnikAHbQg7sB6L0YdGWiBLCnbVc2B8BsrSFLHHIhG1CJCdum3LgrPxIbAt5ZHtfZPv6y4b9QdmCRVuKiyyDY7Yf1Mq23rLFk+2y/bJjUrEvKJgMt3JhThJRpuL4RTJhPspBxn6QMSZKDou2RJTRsr2ATyQ7bptETeP2EYrtQUSZgB9+kkxkyAsZYmirWLSlhHHDlAP7QR7sCyJqDscIOfDDPlW2rz81j/lBsmAuyot9Q+GYE6nHoi1RHDgIEVEm4tjF24bIhm1CMpHh9iFERJQYjtvUlrFoS5Qg7hwIYB40R3TbiF6+nyxxUAD7hERjDgawLeQhS18wjtDl80PHANniIfJjblJrYdGWKAPJsFOQIQYimXEbIVnxXnxEcpKhSCcbtgXJQqZcFB2L6OUTZRNhRduPPvoIZ555Jvr3749LL70U5eXlAICtW7fikksuwdChQzFv3ryQASHdz5G8eFArF84AEI+XdgZk+/rLjH0jDtuemsP8IGoetxG2AclFhnyUIYZwMsZEyRFStN21axfuu+8+TJkyBevXr0e3bt1w//33w+VyYcKECRgwYABWrlyJ8vJyrFq1CgDS/hxFx0FAPuwTFdtBDuwHecjSF7LEIUq2r7/MZOgbGWIgIqL48IoVolCcVNd6hBRty8vLcdddd+G8885Dx44dccUVV+Cnn37C+vXrYbVaMW3aNPTq1QuTJ0/GihUrACDtz1HLuEESERHFjvvNgGxuC1nWXZY4gskYU7ZiX5AsmIvyYt/Igf3QthlFLHTEiBEhv2/fvh29e/fG5s2bMWjQIOTl5QEA+vbtq902Id3PEUXDAZHC8bYE1BLmhRzYD+THXCCSmyzbqCxxEMlExu1ChphkiIHaJiFF22AulwsvvfQSrrvuOlRUVKBHjx7aczqdDnq9HhaLBVarNa3PlZSUxLUeiqJk1IbqjzfemINfI3Kdw+MQsVyfzyds/YOXnUw/JJoH4cuVIRdExSBbO6QqjlSMEaK2EVHjQ1NxyJCbqeiLtrTfSGduyrj+ycbRlnJB9LYpUztk8xgRPCb4fD7o9WK+9kP0fiNVx5d+mXwsER6HiD7xr3tbGScyeYwAUr99JEL0/itaDDKME7L0R1vpk2RiEJkTmSbW9RNetF2wYAHy8/Nx2WWXYcGCBTCbzSHP5+TkwOFwwGAwpPW5eIu29fX1wg7wEqEoCux2OwC1WB0rq9Wqva6+vh4ul6tV4oslDqfTCQCwWCxpW67dbtfW32QyIScnJ23LDmaz2UL6wd8W8Uo0D4DQXLBYLDAYDAnFkCybzQav1wuDwZDWXPBzu91aOxiNRuTm5qY9BkDNA38cDQ0NSfdHornR0NAgfIyw2Wxwu90A0js+RIsDUA/yRcQRPE6YzeaI/V28UpUTXq83qTgSFb7/MplMaVlucLsZDAZhY4TD4QhZ/2SOWVKVC+nqg3BWqxWNjY0AxIwRwe0nanwAAKfTGXJMI2qMCN5/1dfXJxVDosKPaYxGMadIVqsVbrdbimMa/0SWZCSaE8HjVUNDg7AT9+Axy2KxpP24X/QxLgB4PJ6UjVeJ5kP4eY8o4fuwRM+/kmGz2YScAwcL7kePxyNsnAjeh6VivEpE+P7L4/GkPQYgch+W7P48Ef7zL5E5kWl8Pl9Mfye0aLthwwYsX74cb7zxBkwmE0pKSrBt27aQv7HZbEKei1dxcbGwolUi/Ac/JSUlcW0IdrtdS67i4mLt1hLpVldXp7V3vAX2ZBiNRu1gobCwMK3LDmYwGNDQ0AAAKCoqSrgfEs0DANryATUXRJ3gFBQUwOPxwGg0CukPl8uF/Px8AGJzAoC2c0tFHInmhtvt1gq1osaImpoaLQaR/eHPi7y8PCFxBI8TInPC6/XC4XAAUMeroqKipOJIlM1m09ahuLg4bQe0iqJouVBUVCQsJ00mU8riSGZ88J9sprMPwjU0NECn00Gn0wnpj+CcyM/PF5YTDocDtbW1AMSOEcEnZ8XFxULaI3h8KCkpEXZM499/tZVjmkRzwmw2o66uDoA6XhUXFycVR6J8Pp/2AU9xcXHaP3TzH+MaDAZh44Tb7U7Z8Uyi+RB+3iOqLTweT8g+TMSHsKLOgYMF78NycnKEjRNOpzOl+7BEhO+/CgoK0h4DEFqjKSkpEXJ85d9/icyJTBPrRBZhRduKigrcfffdmDFjBo488kgAwMCBA0O+CGz37t1wuVwoKSlJ+3Px8h/8ZxJ/zPHEHfz3Itc5PA5Ry5Vl/ZOJI5E8SHUMyUp0HVK57PCfRUh1HJk+RgT/LzqGtpKbyYwXqYwjEaJyU1GUkGW1lfXP9PFB5H7DH0O0n9MdgwxjhAx5IUMMwctuS+2QqTkhQxyi8yE4hvCfk32/TM8H/++i4hC5/PAYsvn4krkZGUeqli967EuHWNdNyPX8DocDN998M0aOHIkzzzwTNpsNNpsNQ4YMQUNDA1avXg0AWLx4MYYNGwaDwYChQ4em9TmKrq3fV4QyF3NTDuwHebAvSGYy5KfIe8/JQJY4gskYExERkcy472zbhMy0/fzzz1FeXo7y8nK88cYb2uMff/wxysrKMGXKFMyfPx9erxdLly5VAzUa0/octYyDA1EobhPyCZ/lmM7lklzYJ+THXKBwzIlIIttElv6QJY5sx36ITtQxbngMpGJbUGsRUrQdOXIktmzZEvW5Hj164IMPPsCmTZswePBglJaWhrwunc9RZkjnDkuWwViWOIh90Ry2jUqGdpAhBiKiTMDxUi7sDwrGfBBPxj6QMSaiVBH6RWRN6dy5Mzp37izFc0RN4c6BiCgzZeP4HbzO2X5bgGAyzFSSAWdVykF0W4hevsxEtI0M/SFDDLJgW1A45gSlg5B72hIliwNkANuCiDJFNo5X2bjORJQcjhsUjPlARJS9WLQlooTIMGuLAmTpDxlyQYYYKID9QdQ0bh9ElCk4XgXwihXx2BbRic5N9kvqsWhLceFGSLLhDoIoOm4T8mGfBGTzh0uil+8nSxzBZIwpW2XzNuonSxzZjv1A1DxuI20bi7aUMA4O4rDt5cR+kQ/7hIiIiIgoNXhsHR3bhVoLi7aU8ThAkkjMv6Zlc9vIcrsKCmA/EFFTOGbLi/1BFJ0M24YMMQDyxEHUGli0JYqDjAf1ssRBRPLiOBEqG9tDhv1XNrZ7puCl8HIQ3Raily8zEW3D/ojEsYpkwpygdGDRljISB0jxZCgAEFFmycaxIhvXOZOwf0hGzEsKxkKlfNguRNFx20g9Fm2JKKNxx0DhmBNElClYjCGiTMExg2TCfIyOV1O1PSzaEmUgDoqRsr1NZJn5nO39QJGYEwFsiwCOUxQN+4YAefJAljiyHfshgG0hLx7XUGth0ZbiwgGBKJQsxVKKLps/bZYhBiKKxG2TMoEseSpLHESy4bZBlB1YtKWMxx0WEcmMY5Qc2A9ERJQo7kPkwNmM4sk4YSWbJ2lQ28eiLSUsGwcpGddZxpiIAOamTNgXobK9PXhyEyBjTCJkeyFEliKE6LYQvXwKxf4gIiJjvC+oqanBtm3bUF1djYKCAnTr1g19+vRpjdiIpMYDKSIiaoks+wpZ4iCilnF7JZIbt1Gi6LhtpF7MRduvv/4azzzzDOrr6zFw4EC0b98eNpsNFRUVqKiowNixY3HdddfBbDa3ZrxEADgYyECWWSkkF+YCheNYEZDt60+RmBNE1JJsnv1N1BTmZnRsl7anxaKty+XCQw89hMrKStx3333o169fxN/Y7Xa89NJLuOSSS/DEE0/gyCOPbJVgiUjFwTiAbaFiYSwg29efKBOwCCGejO0gY0yUfrLkgSxxZDv2QwDbQl7Z3jfZvv6tqcV72r7yyivo1asXXnrppagFWwDIz8/HbbfdhtmzZ2Pu3LnsMCJKOxnGHRliIPJjPhIRUabjvkwe7Au5sD/Ij7nQtrU40/amm26K+c0GDhyIJUuWJBUQUbw4SJFIzD+58QuXyI99EsDtQiy2g3zYJ5HYJqFEt4eiKNDpdEJjyHaic0BG2d4m2b7+lB5xfRHZV199hcbGRpx++ukAAKvVilWrVsFiseCYY47Baaed1hoxkkQ4MMmHfUKyYm6q2A7ZTZb+lyUOItlxWyGSF7dP8WTsAxljIkqVFm+P4Ddnzhzcfvvt2Lx5MwDA4XDgiiuuwLJly7B9+3bMnDkTjz32WKsFSvLJxvvRybJDkCUO0cLbge1CFB23jVBsD/JjLqiy8ZhORv62yPbjXFKxP4go03DcSr0WZ9pu3rwZv/76K9588038+c9/RufOnbF69WqsW7cO+/btw+TJk5GXl4fevXvj1VdfxZFHHol+/fo1ef9borYi2wckfvEVEcUr28eKbF//YGwLImqKLOODLHGIxDaIju1CsuKHfm1PzLdHMBgMMBgM0Ol0+OKLL/Dvf/8b06ZNQ05ODnQ6HQwGAzuKiCjLybAfkCEGCmB/kGxkyknOcA0lY0wkFnOCmAMBbIsA2doiOB6fzweXy5W2ZXs8Hvh8PgCA0+mEwWBI27L9vF4vALUdHA5HUu+lKApcLhccDkdG38vbZDKlpC9aLNr6Z83u2bMHr732Grp3744tW7Zg+vTpGD9+PHbt2oUXXngBGzZswOWXX44xY8YkHRQRUaaQZcaxLHGQPJgHRHLitkmZgHlKRBQ/l8uF7du3a0XUdPB4PNqYvW/fPuj1Md8FNWXcbrf28/bt25N+P5/Ph+rq6qTfR7R27dqhS5cuSRWfY55pe9ttt+Gkk07Cb7/9hv79+2PgwIEA1Ip6cXExHnroIX4RGZEAPKgmIqJ48NI5IvkpipLRM4zamnSPXzKOlzLGlE6yrb9s8Ygky3GNoijYt28fDAYDevbsmbbiqcvl0mIxGo1CZto6nU7t55ycnKTeS1EUeL1e7Ur/TKQoCux2O6qqqgAAXbt2Tfi9Wiza+nw+Ldn++Mc/4o9//GPI84cddhjuueeeqH9PbY+MOwcZYyIiObZNGQ7iZGgHkiMXRJIlDpJTtueHLGO26H4QvXxqGov4KuaoeLKMlzLyeDyw2+3o1q0b8vPz07ZcnU6n9UWqLslPRm5ublKvbwtFWwDIy8sDAFRVVaFTp04J90uL1dUlS5bgwQcf1O5R0ZxPPvkEV111FTfeLMF+Fodtr2I7EFEiOHaQH3OBiJrC8YGawtygaPw1M7PZLDgSkoW/eB98+4h4tVi0vemmm9CpUydccskl+OSTT6Lem6OiogLTpk3Diy++iCeffDKjq+GUGWSYMcWddYAsbSFLHESykWXmWLZvo9m+/sHYFuTHXKBwzAkiymSsh5FfKnIhpnva3nbbbTj33HPx/PPPY/bs2ejZsydKS0ths9mwe/duFBYWYvz48ZgzZ07SARFRZuABNRERZSKZ9l+yfKBCJCvmqVgytL8MMRC1hHkav127dqFHjx7Q6/VwOBz4/vvvceKJJzb7GovFgkWLFmHixIkoLi4GoN7T12QyaQVSr9cLn88Hk8nU6uuQDjF/EdkRRxyB+fPnw+PxYO/evaipqUF+fj66du2KoqKi1oyRiIjikO1FABliIJIZtxGSFXOTZMXcJBnw6qUAGdZfhhhS6aOPPsKtt97a5L1XfT4fLr74YsyePVt77MUXX8T+/fsxZcoUAGpRNTc3F/v378cFF1yAjRs3wuFwAIi81+1dd92FU089FXfccQfsdjvuuusuvPnmm+jevXuTMebn5+P111/HiBEjcMIJJ6Curg6jR4+G2WxGdXU1CgoKYDabMWrUKDzwwAPJNokUYi7aai8wGtGrVy/06tWrNeIhiltbGyzjle3rL5osB0/MA7nI0B8yxEBEkbhtUiZgnlI4GXKCx9pErcdoNKJ79+5Yt25d1OenTp0KozFQQrTb7ViyZAnuvfde7Z6tN9xwA+68804MGTJEu7fv8uXL8eGHH2LZsmXaa7/55hv89NNP6NGjB6ZOnQoAGDRoEJ555hkoigJFUeDxeHDdddfhmGOOwbJly/CPf/wDgPqFb/feey+Kiopw/vnnY8OGDQCAMWPGYObMmRg0aFDqG0eguIu2RCQeDxooE8iQp4yB/GS4F3q2Y1vIK9v7RpYPYIMpisL7IgomU4FQlrwkCiZLXsoSR6bT61v8yquQWbgzZsxAY2Mj5syZg0ceeQQzZszAnXfeidmzZ2PFihUA1Jm3S5Yswd/+9jftdR6PB2VlZRg+fDgmT54MAKisrMRNN92Et99+G4qiwOv1wuv1olu3bgCAuro6nH322ejXrx8OOeQQHHvssXj33Xfx66+/au+7b98+9OvXLyVtIRMWbSkuHBBJJjygpXDMAZKZTAUAUrFdxGMfBIhuC9HLJ8oU2bytZPO6t3WxfFDoL9q+//772LhxI95//3107twZTqcTgFqQffrpp+F2u7VcmT17No499ljtPZ5//nns3LkTFRUVuPXWWwEAbrcbTqdT+72hoQEjRozAjBkzAADnnHMOamtrceutt2LmzJkAgNGjR2P06NG4/fbbUV1dDbvdjhtvvBEA8Nxzz6GgoCD5RpEAi7aUsGw8+eROiogykSxjF+Mgv2w8hiCi2HAbpWDMB8p0Pp+v1d/fv534fL4m70nbkniu7jj77LOxfft2XHnlldDpdHA4HPjggw/w4Ycf4rHHHkOfPn1w1FFH4Y477sCuXbtw3nnn4e6778aPP/6IV199FX/605+wc+dOPPXUUwCA3bt344ILLsDatWu1mbbB69GjRw/MmDEDxcXFWLx4MWbNmoWjjjoKL774In766SdcfvnlGDNmDADg0UcfjWnWcKZg0ZYoQdl+ACHjpYQkHnOBwjEniJrG4nUoGWMisZgTYrH95cL+kFdTfbNr1y40Nja26rL995MF1JmwBQUFCX0HlU6nw759+3DCCSdEfd5ut+Pyyy/X/vbGG29Ex44d8f333+Puu++GwWCAyWTC8ccfjyeeeEJ73WOPPabd37ZPnz546qmnsHXrVvz973/HGWecAUCdodvY2IgzzjhDK9qedtppKCsrA6AWYnNycjB06FAcd9xxeOWVV7RZuAaDAeeddx4ef/xx3HrrrcjNzWXRlojkwZ23PNgXRJQJOFYRyYfbpdzYP0RykWGblCGGVNLpdOjatWuzX0QW7IMPPsAvv/yC3bt3Y926dejVqxf0ej0+++wznH/++drfHTx4EFdddRUAIC8vDyeeeCI2b96M/v37Y8KECQCA6upqPPTQQ1oh1uv1wuPx4JdffkH//v1xzz33QKfTYcWKFSgrK0NJSQn++9//onv37tpyNmzYoN0jty1h0ZYoA7W1HUQmk6UvOPOZwjEP5MB+CGBbEMVO9PYievlEspFtm5AtHpFkbotevXq1+u0RnE6n1gYmkwkmkymh94mlHYP/Zs+ePaitrYXNZsOePXtQUlICABg+fHjETNtwpaWl6NKlCyorK7XHpk+fjsrKSiiKAo/HA6fTiby8PADA5s2b8fLLL6OhoQH//Oc/UVtbi0ceeQR2ux2KosDn88HtdocUcdsKFm0p48k8SBNRduK4JJ9s75NsX3+ZsC/kww8+KRreviSUDDHJEANRvFr7Un29Xq9tG8ksK97t66abbsKqVavwzTff4Nprr0VOTg7WrVvX7Exbv/LycmzatAmbNm1qMpZ77rkHp5xyCgDgu+++w/79+7Fnzx5MmjQJ9fX1OProo3HllVdi8eLF2LJlCw4//HBt/d1uN3JycuJaH1mxaEtx4Y6SZMJ8lJsM/cMYSCbMhQBZ2kKWOERjO5BMmI+R2CYkC37I1XbFMiPY6/VGPHbw4EFcffXVmD9/PoDImbaPPvpoxGtcLhcuvPBCTJo0KeI5RVFw9dVXw+VyaY9dd911ANT76k6cOBHvvvsuPv30U+h0Ojz++ON49dVXcemll+LJJ5/EvHnztBm6bQGLtpSRRO0guGOKTpZ2kSUOEoc5QLFgnmQ39r9c2B/yYF8QxYbbCoVrCznh8/la/CKysWPHhjy2d+9efPrpp5gwYQJ69+6N8vLykOedTieqqqrQu3fviGW1RKfTRTy2dOlSvPfee2hoaMDQoUPh9Xqxfv16KIqCMWPGYObMmZg5cyaWLFmCHj16tLiMTNBi0dbtdmPy5Ml4+umnAQAVFRVwuVwh0649Hg969eqF7du346effsK4ceNaL2KSRlsYmNoCFrCJqCWyXOLJcYNIHtweKRMwT4k5QJQeXq+3xS8i83g8IY+1b98eDz74IC655BIAapE22D333IPPP/8cTz31VMjjDQ0NeOutt7BmzZqoy6qsrMTFF18c8pjT6cSf/vQnbabtmjVrcMUVV+Coo47C3/72N5jNZsyZMwf33XcfysvLs6doazQa8fPPP2u/33fffaiqqkLHjh21AVSn0+Gmm27C/fffj8suuyzmhdfW1uKSSy7Ba6+9pjVoWVkZli5dqv1Nr1698OGHHwIAtm7dimnTpmHXrl0YN24c7r33Xq363hrPETWHBxBEkbhdEMmP26k82Beh2B4kq2zNzWxdb1mxPwJkaAsZYkilM888E2eeeWaTz8+dOzfisauuugqKomjF2vPOOy9kNu7s2bORl5cHg8EQ8rrp06fjoYceinrfWUVR4PV6I15z8803az+PHj0ao0ePRkNDA4qKikL+bvbs2c2sZeZp8S7FOp0ONpsN77//PioqKgAAN954I+644w7ceeedePLJJ7Fs2TJs3boV48aNw+233x7TgmtqajBhwgTs2bMn5PGff/4ZixcvxsaNG7Fx40a89dZbANR7XkyYMAEDBgzAypUrUV5ejlWrVrXac0Qya2s7iEzGWYRE1ByOCwFsC6KmybZ9yBYPESDPlUMykCUeWeIgORUWFkYUXwEgLy8vJV8UFl6wbYti+mo5t9uNtWvXYuLEifj++++h0+nwwQcfYO7cubjyyisxfPhwNDY24pZbbol5wZMnT8Z5550X8pjH48HWrVsxZMgQFBcXo7i4GIWFhQCA9evXw2q1Ytq0aejVqxcmT56MFStWtNpzlDm4oyCSs3gsSxwiyNAf2dz+FIn5IA8Z+0LGmESRpS1kiSObsUBIlBm4vVBb1uLtERRFQbt27fDMM88AAK6++mps374dZ599Ns466ywA6m0OvvrqK/z5z3/G4sWLtUJrc8rKytCzZ8+QqctbtmzRbiBcWVmJoUOHoqysDN26dcPmzZsxaNAg7Vvg+vbtq93kuDWei5eiKBk1WPjjjTfm4NeIXGdRcbS19U80D1IZQzLClysiDhnaoTXiyNQxQoacaCoOkTFkc074+Xw+4fuN4BjSLZX9kIpckKUtRMQhyzbBMSJA1PgQTIb9V6rbIdGckKE/wped7jhkyIfw5SY7XmbyGBG+bNH7jmi/p0v4umfzvqOpGESdA4ladvDym/o9Ve+baZrrl1jXLaYvIgv+0rGjjjoKmzZtwi+//BLyN1OnTsX333+Pe++9FwsXLmxxwT179ox4rLy8HH369MEDDzyA9u3bY9asWXjwwQexZMkSWK3WkBsJ63Q66PV6WCyWVnmupKSkxXUIVl9fH9JOslMUBXa7HUD0b+VritVqhdvtBqDePFrU/X9tNpv2c319fcQNr1tLfX291m4+nw8WiyUtyw3X0NCgxdHQ0JBw7iWaB4DaB16vF4DaLuE3JU8Hj8ejxQ8AFosFRmOLw1pKBfeF1+sVlhNWqzWkL5ONI5kxorGxEUByuZkor9cbkROpuPQmXk6nM6X9kYjgnFAURWhO+F9nNpthMpmSiiNRNptNG6fq6+tj+tbaVGhsbNTW3+PxSLHfSDYnUzU+iGKz2bT+T+cxhJ/D4RA+PgChOZGKY5pE8yI4DoPBgNzc3KTiSET4+OA/vkknn88n/JgmeLx2u91S5ITRaBSyHwdC2yPd5z0yHOMC6rbhj8N/npyoVI0RMhxrizjGDW4/QMz+CwjdhwFqbiazbSSaF8Hn5akYrxIRnhM+nw8+nw9erzet+xGv1xvygYIIwctNxbqLWo9U83q98Pl8aGhoiNheY13HFkd+s9mMjz76CA8//DAaGhrw6KOPwufzQa/Xw+1244knnsA999wDnU6HP/zhD/jqq69gtVpjmm0b7sILL8SFF16o/T59+nSMHDkSVqsVBoMBZrM55O9zcnLgcDha5bl4i7bFxcVR79UhK/9GXVJSEtfgWFNTA5fLBUC9P0m87ZQq+fn52s/FxcVpO8D3eDzaxpafny9s/Z1Op1Y8T6YfEs0DACgoKNBOcIqKihLa5pPldrsjciHdBSH/fb8BIDc3V1hOOBwOrT9SsW0mmhvBJzVFRUVpbw+v1ytsfAjmcDhQW1sLQM0REXnhdru18aqgoEBYTlitVu21IvcbBQUF2rhZVFSE4uLitCzXZDJpJxI5OTnC1t/r9cLhcABIPh8ydXzwy8/P1w6URYwRZrMZdXV1AMSND0BokTAvL0/YGNHY2Kid4IkaI4LHh+Dbs6WTz+cL2X+VlJSkvUin0+lgtVoBqHkqKieCxyuR+w2bzSZs/+XxeIQf4wJqkdT/IZterxey73C5XFKcf/q3DUDMPkxRFCmOcYP3YUBi55HBkjkfDZ4UICIvwvdfhYWFqK6uhsFgSGttyGAwaMc1er1eSF0q+EOMVC0/k+prTTEYDNDr9SgqKorYXmMtbsd8JLBx40Y8/PDDAICLLroIs2bNwvfff4/ff/8dl112GXJycqAoChwOR8oOdIqLi+Hz+VBVVYWSkhJs27Yt5HmbzQaTydQqz8VLp9MJm3WaKH/M8cQd/Pci1zl4uemMI3xZItc/Vf2QSB4EvzYVMSQqWn+kO462mBPh7xnve4nMCxlyIny5MsTg/z1V75mp+w1RcbTF9WcuZO7yWzOOTM0LGWLwLztaTOlcPnNCjjja6vEM8yH5GJr7PV0xpDo3Mz0vZIhH5Pmooigpyc3Kykps2bIFw4cPj/t9KioqsG7dOlx77bURz/k/lG2uzud2u6EoSsSkzmiqq6vRoUOHmOJqLidiXbcW5/RXVFTgrbfeQm1tLY477jgA6icKM2fOxMKFCzF16lTYbDbcc889qKqqwkMPPRTTgqOZM2cO3nvvPe33TZs2Qa/Xo2vXrhg4cCB++OEH7bndu3fD5XKhpKSkVZ4jIopFpt9nh4goXTheElEm4ZjFNgjGtpAL+yP1amtr8e2336Kmpibm13z00Ufo168fBg8erP0bMGAA+vfvj/79+6Nfv3647777Ql4zb948fPvttxHv5fV6cffdd2sz66M9H8zj8Wh5UFVVhVdeeSXq6/7v//4PJ510Ek444QTtX79+/TB06FDt95NOOinq6y+77DKsXr1a+91iseDUU0+NmPzZmlqcafvmm2/iX//6FxobG7FhwwZs3boV7dq1w9ixYzF37ly4XC7k5+dj0KBByM3NxTHHHJNwMEcffTQWLFiAQw45BB6PB2VlZRg7dizy8vIwdOhQNDQ0YPXq1RgzZgwWL16MYcOGwWAwtMpzJK/Wusk1USZL5RcCUPLYByQb5qQ82BfykbFPRMckevlE0TAv5RNtlqWIGCg57777LmbMmIHu3btj+/btmD17NkaPHo2tW7di2rRp2LVrF8aNG4d77703pL+NRiO6deuGf/7zn9pjOTk52t9MnTo14lY/J598MiZMmIDJkyfjv//9L3777Tfk5eUBUGefXnXVVTAajdqX3r355psAgHPOOQc1NTUwGo3weDxwOBx45513cPjhh8NgMDR5xf+1116Lyy+/POT+6EOGDMFrr72Go48+GgCwc+dO9O7dO+K1OTk5Ia/77LPPcPzxx6NPnz5xtW8yWiza3nnnnZg8eTIuuOACmEwmLF++HC6XC9u3b8czzzyDBx98EHv37sX06dNRVVWF6dOno6ysLKFgxowZg/LyckycOBEFBQUYOXIkJk+erAZqNKKsrAxTpkzB/Pnz4fV6sXTp0lZ7joiIksMDKJXIbzmmABnaQ4YYKBT7RMV2CGBbiMc+YBvIjv1DbUl9fT3KysqwbNkyHHXUUVi9ejUee+wxjBo1ChMmTMApp5yCJ554Ao888ghWrVqFSy65RHttLF/IFz4p8pRTTsFLL70Ej8eDyy+/HPv378eTTz6JmTNnwmw249dff8Vjjz2GmTNnokePHtrrPvzww4j3/vDDD/HWW2/h4MGDqKmpwRNPPAEA6NChA6655hoA6vZ6xRVXYMqUKTj55JMj3mPHjh04//zzsWrVKhx11FEAoH2PF6AWkr1eLwwGA9555x38/vvvGDVqlPb6yspKPPLIIyHfz5VKLRZtgzvhrbfewty5c7F06VJ89913mDJlCj766CPk5OTgtNNOw2mnnRb3Nxhu2bIl5PcpU6ZgypQpUf925MiR+OCDD7Bp0yYMHjwYpaWlrfocUSbI5oMGzrqmljAniOQkctvkuCAX9oc8ZOwLGWMSQYZ24IfQYsnSDrLEIZtMbhebzYb77rtPK1j269cPFosF69evh9VqxbRp05CXl4fJkydj5syZIUXbWGZZBxdtrVYrfvvtNxx77LEAgG3btuHOO+/E9ddfj/nz5+PII4/E+PHjcf755+Pqq6/GpEmTcPHFFzf53kVFRejWrRsURUFOTg569OiB33//He+8845WtNXpdDj99NPxwAMP4J133omYkbt48WIcd9xx2voDwIwZM/DGG29AURT897//hc/nw9q1a/HDDz/gk08+0WYGA9C+46u1xPxFZHfccQdsNhu6dOmCxx57DK+++ipyc3Nx3XXXYcuWLRg5cmSrBRmsc+fO6Ny5c9qeo1CZPBilgizrL0scfrLFQ9mLuSg3WfpHljhEyfb1JxXzgCh23F6IqK3q2rWrNkvU7XbjpZdewllnnYXNmzdj0KBBWoGyb9++KC8vD3ltvLfG2Lp1KyZOnIhLL70UN910E8rKyjB9+nTU1NTg66+/xsSJE7Fu3Tp07twZL7zwAl555RVceOGFeOSRR7Bu3TrodDq43W7k5ubC5XLhrLPOwoMPPojPP/8cP/74Iy699FJ8+umnEZNDJ0yYgDVr1uDZZ5/FX//6V+3xTZs2Yc2aNXjjjTdC/n769OmYMWMGrr32Wlx11VUYMWIEJk+ejKuuugqPPPIIxo8fr90a1h9Pa4m5aBtelL3uuusAAEcccQSOOOKIlAZFmYEHL0QUjuOCnDirUZxsX39qHvMjFNuDiIgyRVvbZ23evBnXXHMNTCYT/vnPf2LhwoUhtyfQ6XTQ6/WwWCwoKSnRHtu/fz9OO+20qO9pt9tx+eWXa78PHjwYK1aswF133YXx48fjlVdewZo1a7Bs2TLMnz8f1dXVqKmpwaxZs/D444/jkUceAaDOfJ0xYwaef/557Ny5E7Nnzw5ZTl1dnXbVvM1mQ35+fsjzZrMZN998M3744YeQx5cvX45x48ahf//+IY+bTKaI34cOHYpx48bhiy++wK233op33nkH7dq1g9vthtlsbrF9ExVz0Xb9+vU45phj0K5dO+2xb775BtXV1TjrrLNaIzYiklhb20klil8ARrGQ4UsaiETjGEnUNNm2D9niEYFtwDYIJltbyBIPb5vRgm+/Bd59F4jzNqLxMIe3RW4ucP75wHHHxfU+ffv2xSuvvIJ58+Zh2rRpOPTQQyOKkTk5OXA4HCFF2y5dujT7RWThevTooX252IEDBzB79mwcdthhuPbaa3HUUUdh4MCBuOmmm/Dkk09i+PDhITGsXbsWZWVl+PHHH7Fx40bccMMNAIDy8nJ0794dgFq09c8OdrlcahuZzRg7dizGjRsXEstDDz2k/Y3L5YLP54s6a1av12sTV8866yzk5eVhw4YNOP300+F2u+W4PcI777yD7t27Y+rUqXjuuedw8803Y/z48di3bx+WLVsGm80GnU4Hl8uFW2+9tdUCJpLlPqYZs6MgorTj+ECyYU7KQ8a+kDEmUWRpC1niECGb152ax9ygaKTOi48/BiorW3UREdNCLBbgo4/iLtrqdDr0798fc+fO1W4HsG3btpC/sdlsIbNQY2n7aH/z6quvYuDAgRg8eDC+/vprfPTRR3jqqacwevRoAMC4cePQp08fVFVVabN9v/zySxQVFeHYY49FVVUVnn/+eZxzzjno3r07fv75Z4wdO1aL0T/TdtGiRXjttddgNIaWPq1WK6655pqQ7/Byu90YMmQIFi9eHBGv1WrV7oVrs9nwr3/9C5s2bcKQIUPkuT2C0WiEyWTSOm3Lli0wmUzIy8vDiy++iMGDBwNQv72NRVsiovSSccavLHFkK7Z/KBnaQ4YYZCFLW8gQB2fiiydDHhAFY07KTUT/MCcy1MiRwNq1rTrTNjw3dLm56nJj9OWXX2L9+vXavV79Xxx2+OGHY8WKFdrf7d69Gy6XS5tlG23ZsXC5XFiyZAmWL18Ol8uFl156CU899RSef/55DBo0CFdddRXq6urw3nvv4ZFHHkGPHj1QV1eHWbNm4dRTT8Xy5cuxc+dOmM1mPPnkk5g2bRq+//57PProowBCi7Z33HEH7rjjjogYhgwZgtdeew1HH310k3Hu3LkTdXV1WLRoEWbOnIkvvvgC7777Lh577DGcf/75ePPNN2E2m+FyueSYaevnX3m9Xg+dTqcdZM6ZMweAessEImpdMhboRMjmdSciosRw36FiO1A4GXNClphExyF6+UR+zMXommyX446Le8ZrvJwOh/az0WiMmFXaksMPPxy33norDj30UJx66qlYsGABTj75ZJx++umYPn06Vq9ejTFjxmDx4sUYNmyYVtQFAJ/P1+L7e73ekN/Xrl2L448/HlVVVbjyyisxZMgQDB8+HHl5eSguLsbo0aPx9ttv46233tJmsB48eBDV1dX4/fffYTKZ0K9fPxx//PF46aWXMH/+fJxzzjlaMTnaPW3jdf311+O7777DmWeeiXPOOQennHIK6uvr8dxzz2HmzJkh9/CV4p623377LQCgvr4ePp8PdXV1UBQFNpst4m85U6Bt4yBN0ciSF7LEQeLIcvsUIplxuyCZMB+JMgNvSUcUqq3kZufOnfHkk09izpw5mDdvHk455RQ8+uijMBqNKCsrw5QpUzB//nx4vV4sXbo05LU+n6/FLyLz37YAABobG/HMM89gwYIFOProo7FgwQIcd9xxuOuuu/DRRx/h1VdfxRNPPIEffvgB06ZNw5QpU9CjRw8ceeSR+OKLLyLqjTt27MDf//53rF69Gr/99hu2bNmCdevWYeLEic2us9frjSgmB5s2bRq6du2q3RIBAHJzc7FmzRro9XpYrVZ8/fXX2Lt3L6xWKzp06NDs8pLRYtH2888/x8MPP4wjjjgC48aNg06nw4knnggAuPPOO/Hwww+3WnAkt7YySMUjG9e5KWwLIoqFjFcGpDMOWdaZKBNweyEiig3HS0ql4cOHY/jw4RGPjxw5Eh988AE2bdqEwYMHo7S0NOR5r9cb8UVkZrNZu1fs1KlT4fF4tOe++eYbdO/eHccccwwA4LjjjoPFYsH69esxcOBATJkyBQaDAQsWLMBzzz2H6667Dm+++Sbat28fUbCtrKzE6tWrsWjRIhQXF+PXX3/F3//+d5x77rk499xzm11fp9MJZzO3rOjTp0/Ux/3rZTabMW3aNHTo0AH33HNP0jN7m9Ni0fakk07CO++8gxkzZuCDDz7ArbfeinfeeQdnnHEGZs6cif3797dacEREspOxICUS2yA60fesZL+QDJiHRE3j9iEf9ol8RPYJ84GyVefOndG5c+eoz5155pkYMWIEXC5X1Ofnzp0b8vspp5yiTQL1KykpwfPPP48hQ4YAULc1g8GA22+/HZMmTQr5srDwuFavXq2dY51wwglYvnx5TOv0yy+/xPR3TTGbzfjqq6+Seo9YtVi0NRgMIfesCMZbIZAIslz+zB03ETWF4wNFw5NN8dgO0bFd5CO6TzheEcC+oJbxXJziFe2eu/6CbbimCrZ+2VCTbL4FoH6z21//+lfU1NRkRYMQZQLupCgTME+JqCmyjA/8BnAKx/4hahq3DxXbQS7sD2rLWpxpe/DgQTQ2NmLDhg249dZbUVFRgXHjxuHAgQN45JFHcOihh0JRFEybNg0AUF1dDafTiZycnFYPnoi4kyIVb9NA4ZgHodgeAWwLuYi+fQpxm/CTsR1kiSndcciy3jJgW6jYDnJj/1BrabFo261bNzz11FOoqKjArFmzUFFRgTPOOAMTJ06Ez+dDYWEhduzYAa/XC5/Ph549e/LAkyjLyHJZDHeWxJyQWzb2RzauM2UGWXJTljiIZMbthGTBXAxoqi3YRuSXilxosWjr17NnTzz33HNYvXo15syZg8cffxynnHIKAETcSJioreIATNQ8biMUTsackDGmbMB2p5YwR0gmzEeSGfNTPv7vgnK5XMjLyxMcDcnAbrcDAEwmU8LvEXPR1m/MmDE4/vjj0aVLl4QXSpmLl2CTH/tfxW2CYiE6N0Qvn4iImsdxmkhu3EblImN/GI1G5Ofn48CBAzCZTC1+iVaqOJ1O7WePxxP1i75ak8/ng9vtDvk9mXVXFAVerxcGgyFjr+JXFAV2ux1VVVVo166dVtBPRMy9Gdz53bp1S3iBRMni5c9ERJSJ+G3sAbLcVoeI5MPtlJrC3CCZ6XQ6dO3aFdu3b8fOnTvTttzggqnBYEhbsdhPURR4PB7td6PRmHSxNdnCryzatWuX9ITXmIq2DQ0NuOSSS3DbbbfhrLPOwv333w+z2RzxdzqdDscffzzGjBmTVFBEmUCWk28evBDJh9slUWaQYVtlDOLJeFwlOg7Ry/djHGJl63q3hO0SIENbyBSD2WxGnz594HK50rbsHTt2aMtv37492rVrl7ZlA+rtIPbs2aP93r1796j1wlgpioKGhgYUFRVl7ExbQL0lQjIzbP1iKtoWFRXh3nvvxZNPPol27drh119/xTXXXINFixbhlltuwcKFC3HLLbdg3759mDFjBs4///y0T8kmouwjww5aFjzhpHBs/1BsjwC2hVhsfyLKVIqipL2IwjFTxXbIHHq9Hrm5uWlbnsFggM/nA6AWjdO5bECdvBk8KzY3Nzfpoq3T6URubm5GF21TJabKqtVqxamnnoqTTjoJTqcTXbp0weWXX45ly5Zp/19xxRXweDzweDzwer0s2hK1YbIeNMgaFxFlL45LJCtZclOWOIh9IRPeDo5Iftwuo2O7pFZMldUTTzwRubm5WvX8D3/4Q/Q3Mxpx7733pi46IsoIHJhJFjzJkQ9ngZMf252IMgnHLArGfFCxHeTG/ml7Yira/vTTTwAAi8UCo9GISZMmtWpQRLKSZRCUJQ6SsyAlEtuAiGTGMUpu2dw/2bzu4dgWRC3jdkKUHWL6OjZFUfDaa6/hrLPOwq5du/Djjz/i8ssvR0VFRcj//n/UdrFAJR/2A5H8RG+nopdPKlm+wFIGssWT7dgf8hHdJ6KXLxu2hxxk6QdZ4shm7IMAtkXbFtNMW5vNhi+++AJLly7FEUccgSVLlsBsNofcbBhQk8XhcLRKoER+vPyZKDNk87aZzetO8mN+isX2lxv7Ry7sD7HY/tQSGXKEMYiVzeueDjHNtC0sLMTTTz+NDRs2QK/X45BDDsHRRx+Nuro6/P777+jXrx9qa2vRp08fHHfcca0dM5EUODgRkcw4RoVie1A0MuSFDDFkO/aBiu0gD1n7QkRcsrZFurEd5MM+oXSIqWgLADqdDs8++ywA4IorroDb7cbBgwdRUVEBl8uFxYsXo6ysrNUCJSIKxx1lAG9dQiQnbo8kK+YmUfO4jRBF4nZBLWGOpFZMt0e46667cOSRR2q3QzCbzZgwYQKqq6vhcDjw1VdfwW634/XXX0dJSQkmT57cqkETkViy3qJCljiIKEDG7VLGmLIB252iYV4QtYzbCYVjTlA0zIu2J6ai7ahRo7Bo0SI4HA5s3boVPp8Pf/nLX/DNN9/gwIEDGD16NABg7969aNeuXWvGSySULIMgZ1XKg+0fSnR7yPqBAqmyvT+yff1lwD6QG/uHAOYByYl5SUQixFS0Pe+883DeeedhwYIFuPrqqzFt2jQcf/zxcDgcqKysxPHHH9/acZIkZCwWyhIHEXORmsLcEIvtL18byBZPtsvm/pBl3WWJg6Jj/8iB/UAUSfR2IXr5bV1MRVu/O++8E2eeeSYGDhwIABgxYkSrBEWSUxRApxO4eA4KRCQ32cYp2eIhsZgPAWwLkpXo3BS9fD9Z4iAKxrwUT7bJZDLEQG1TzF9E5ucv2FJ2yt2/H4e//DK6rV2rFm+zGAdmouZxGyHidkCReBsXubE/yI+5IEcbyBADNS2b+yeb1705bJfUirtoS9mt+5o1MNlsKNq+HebffhMdDmU5nvhGx3YggHlARM3jGEHhmBPyYF9QOOYEUXZi0ZbiYnA4tJ/19fUCI8luonfaLJaSrJiL8pHt8jUSh/1P0TAviFomy3YiSxwkDnNALuyPto9FW0qYzu0WHQKBA7VMRPaFLHkgSxx+ssVDchCVF8zHAPYBRZPN/SPjunM7DZAxJkov2XJAtnhEY3uo2A5tD4u2lDCdxyM6hLTjIEjhmBMUC+YJicYcJIqO24bc2D8UTJZ8kCUOkgdzgloLi7aUOEEzbXlpPhHJTrZxSbZ40i3b1z8c2yOAbaFiOwTI0hayxEFEJCMZxkgZYpAB26F1sWhLCcvGmbZEFDvuwIm4HVDLeAk6kZy4jcjRBjLEQE1j/1A45kRqsWhLcVF0Ou3nbL+nLe9fKh7bgahp3D7kxH4h5gARtYTjBIVjThBlJxZtKS6K0aj9zJm22UvWW1TIEodobAcikhHHJoqGeSEP9oW82DfisQ8oEzBP2x4WbSlmiqLAZzAEHmDRVgocmMWSpf1liUM0toN8gvuE/UN+vCUARcP+kQu30wAZYxKB7SAPbp+hZI2rtWXremcTFm0pLiEzbbPw9ggcFKk5zA/52kCWeGSJg1TZ2B/ZuM4Uu2zOD1nWXZY4ZMN2oWCy5IMscZA8sjknsnnd00Fo0ba2thZnnHEGdu/erT22detWXHLJJRg6dCjmzZsXkgDpfo4iyXB7BFkvzSciIjnxHuShZIyJiEJxOxWPfUBEzeEYER3bJbWEFW1ramowYcIE7NmzR3vM5XJhwoQJGDBgAFauXIny8nKsWrVKyHMUnS/LZ9oSUeyyeYct27rLFg+RLERsG9we5cb+IZILt0m58TYNRK1LWNF28uTJOO+880IeW79+PaxWK6ZNm4ZevXph8uTJWLFihZDnKLrgmba8p6043EkREWUmjt8Ujie8JAvmBIVjTsiDfUGUnYwt/0nrKCsrQ8+ePTF79mztsc2bN2PQoEHIy8sDAPTt2xfl5eVCnouXoigZNZD6440nZkVRoOgDdX6dxyNshkr4F9ukK47gZfl8PqEnWqmII5E8CF9+sjEkI3y5IuKI1hY6nS6tMYTHkYptIhW5IWJcFDk+yBZH8PYgMidSHUciZNlvpHvZTcWRbAzMhcyPoak4UvF+mZYXMvdHprdFpuaEn8hjmvBj2raQl5l6bClLHLKcf8kyTsg2ZorKTRnGCllyItPEun7CirY9e/aMeMxqtaJHjx7a7zqdDnq9HhaLJe3PlZSUxLU+9fX10Osz53vdFEWB3W4HgJgLTIqiwBQ0u1ZxOmGxWFolvuY4nU4tdgBoaGhIW5HMarVqy/bnigg2mw1Op1P7PdE4EskDAPB4PCF9kJOTA5PJlFAMybDZbBG54PP50hpDQ0NDSAwWi0VI0dZqtWo54XK5ks7NRHPDZrNpfSBiG7Hb7SH9UV9fn9bl+0XLi5ycnLTGELx9pKIvkskJr9ervYeIcdPr9Yb0h8lkgtlsTsuyo+WCiOOF4DgMBkNS/ZBoLtjtdm18MBgMyM3NTTiGRLlcLmHHEMHLFD0+AKHHNP44kpHMGOFyuQCoxxfpHiPCxwez2SzkmCba/ss/dqZLeE7U1dXBYDAk/H6J5kRwHCJywi94/6XT6ZCfn5/WZYvOByD6OJHo9pFMPjgcDgDy5IOIY1yHwxGx/xJxPFFfXx+Rm/4xPBGJ5kX4vjTZ8SoRNpsN7v/dMlKWY1yj0Zj2Y4po+y9PEldlJ5oTmSbWuoWwom00BoMh4iQqJycHDocj7c/FW7QtLi5O+yCRDH9Vv6SkJOYNwefzwRX0twafL+52SgWHw4Ha2lrt96KiorTFYbVatbbT6/VC1h8I3SkVFhYmHEcieQAAbrc75MA1mRiSodfr0dDQoP1eVFSEoqKitMbgcDhCdkrxtmWqBOeEyWRKuj8SzY38/HxtByQiL4xGY0ihNp3jQ7Dggw1Abcd0H0AFbxsGg0FYThQUFGjbSH5+vpD+8Hq9wsYsj8cT8iFbSUmJkJMsq9Wq/Ww0GpNa/0wdHwD1g9/gXBAxRvh8PjQ2Nmq/ixgfgNAiuk6nEzZG1NbWwvi/22/l5OSkvT88Ho/wnAAi91/FxcUoLCxMawxOp1MrQABqXyZbtPW/T7xFOj8ROeEXvP9K95hlMBiEH+MC6jF/8D6suLg44Q89E82H4MlRZrNZWD7k5+drRVsR+zCz2Yy6ujrtd1FjVfg+rLi4WLtyORGJ5oXD4Qj5ICPZ8SoRNTU1WsG6oKAg645x/aLtvwoKChJ+v0RzItPE+kGcVEXbkpISbNu2LeQxm82mFSDS+Vy8dDpdxiWUP+ZY49bpdEDQFG6d2y1sncOXm644gttLdJ+nKo548yDaMkW1hQxxyBBD+HJTFUOyuZHN/eFfdnO/p2P5zInQOKLF1NaX3dRyk40hU3Mh2nKzdZxKdU4Ev2cm5YXM/dEW2iITcyI8FhFxyJAPrRFHJudDa4yZyS6/LeRE8HvE+z6i20N0TjQVg6g4UhlDojmRSWJdN6mu5x84cCB++OEH7ffdu3fD5XKhpKQk7c9RdLrgoq3LFVLEJSIioua19ftzEWU6WbZRWeIgEo3bAsmKuRkd2yW1pCraDh06FA0NDVi9ejUAYPHixRg2bBgMBkPan6MmBBdtfT4giXuVEFHbxh22PNgXJAPmoSq8HUR+qQ4REWUGWcdsWeMiaiukuj2C0WhEWVkZpkyZgvnz58Pr9WLp0qVCnqPodOGDstMJCPiyBhnIsoOSJQ4RZDnxDY+hLV/GQUREbRP3XyTDcRQgTxzBZIyJiCgcx6q2R3jRdsuWLSG/jxw5Eh988AE2bdqEwYMHo7S0VNhzFEX4N9wl8U2RlLlkLJaKIuO6yxgTpZ8seRAchywxkXicXUp+HCPkJUN/yBCDCLIca2dr+0cjW1vIFg+JwTxo+4QXbaPp3LkzOnfuLMVzFKAoSvSZtkQknCw7bNFxyHKSQ3LLxrzIxnUmyiTcRikTiM5T0cv3kyUOEWRdd9FxiV4+tV1S3dOWMoCERdt0DpCyDMayxEFEseE2m71k7HsZYyISRdbtQda4sgn7gIiawzFCxXZoXSzaUnzCN0iHQ0AIHBSIMkE2b6vZvO4ykqk/ZIqF2B8UiTlBJBdukxQN80Je7JvUYtGW4hJxewSbTUwgREQUMx48EclDltu4cFwgIsocso7ZssZF1FawaEvxCf8iMqtVTBykkWFHyRPOABljIiKSgQzjowwxEMlKlu1DljiCpTsmGdsg28nWJ7LFQ0Stg0VbikvETNssLtpyRykfEX3CPCBZyZKbssRBcmFeyIcfwJKMmB9isf0pHHNCbuyftodFW4pP+CDQ0CAmjiwnejAWvXxqnsj+EZ0bopdPzZOlf7L9CyxliYkoXDbnpozrLmNM6STT+ssUi0iytIMscVCA6D7J5g8+ZYihLWPRlmKmKIoUM21luRcckUy4HUQnS7vIEgeRKLJuA7LGRenDHCCKHa9qIwpgbkbHdkktFm0pPll+T1sOQEQUC44VcpGpP2SKhYgiybKNyhIHERERicOiLcUlYqZtlt8eQYYDahliIIqGuUlE1LJsvqSSKBrmJlEkbheh2B6ULVi0pfhIcHsEIpll8wFENq+77Ng3JJtsLlTKEEM0ssZF6SNjDsgSkyxxEPnJkpOyxEHUVrFoS/EJvz2C3R75GLU62XaOspx8y9YuRMTtUhbsB5IVvyRPlc3rTpGYD/Jhn1BzZDkfpraHRVuKS8TtERQFsNnEBEPCcOcQwLaQC/sjQMa2kDGm1ibjOssYExHJJ9vHimxff0C+NpAlHlniEEHWdZc1rnRjO7Q9LNpSXCKKtoDw+9pyYCIKxW0iQJa2kCUOUrE/0o9tTrJiblImyOY8zeZ1J7kxN1W8+rV1sWhLsWvqNghpvq+tyEFAxgFIxpgo/ZgHcpGxP2SMKV1kWneZYiH2B0WSJSdkiYMoWLbmZbauNxGJx6ItxUxpqmgreKYtERERxYcnoPLgffCIQsmQm5w5RiQ3bpOULVi0pdg1VbStq0trGDLhzkIsGdtfxphEYVsQUVOyuVApQwzRyBoXERFxjCbKVizaUuyCirY+fVDq1NYKCCa7ybbTliUeEXHIsu4yYFtQNMwLIqLMION4LWNMRDKQZduQJQ7RZGkHWeKg1GHRlmIXNAC4i4sDj7Nom3W4MyCSn4zbqYwxtTZZ1lmWOIjCBedmNuepjOsuY0zZQpa2lyUO2bBd5JOtk3dkiIFaF4u2FLugmbaeoiIoOp36S02NoIAI4EBNkbI5J7J53Ylkxm2TiChxHENVbAfx2AcUjvcAb10s2lLsgoq2isEAb1GR+kuaZ9pyUCCKxO0gOlnaRZY4SCWyP5gLcmF/kKzHlbLEIUI2rztFkiEfZIiBiLITi7YUMyW4aKvTBYq2VivgcgmKKr24wyaKXTZvLzKue7ZeNgbIE4dM2CbyyOYvZSOSlazFfNHYDuKw7YmyE4u2FLugoi10OniD72tbV5f2cGTAEy0Kl819k83rTkTxyeb9pwwxEEXD3CTKHLJsr9m8PydKBxZtKXbBRVu9PrRoyy8jy2rcWRMRyY3jJbWEOUIyYl4SUSbgWEWtxSg6AMogwd/uC4QWbfllZCSBbL78m4jkxDGCKHbZvL1k87pTJOZDdLK0iyxxkFgy5IGMt3KRIYa2hDNtKXbBG59eD09JSeD3qqr0x/M/2T4osFBJRNHIuJ3KEpMscWQTWdtc1riyCftAXuwbeWTzVW0yxEABsvaHrHFlA7Z962LRlmIX9kVk7k6dAs9VVKQtDA4KRM3L5m1ElnWX8VNvkoMsuSBLHESiybotyBpXOsiw7tyPUzD2PxGJwqItxS78i8gKC4HCQvX33btDZ+K2UbLssGWJg+TBnIiO7RKQzW2RzesuK/aJeOyD6NguRE3j9kFElF4s2lLsgmfa6vWATgf06KE+YLUCFougwIjkwYPZALYFMQdINjLmpCwxyRIHUTDmJZGK20Io0e0hevmUPVi0pdiFzbQFECjaAsCePemNRwIcrMXipWtyYfsTyYvbpzzYFyQr5iZRbGTYVmSIAZAnDtHYDtRaWLSl2IUVbRVFCS3apvG+tiSXbN5JybrussZF2S0b8zIb15koHsHbSDZvL9m87kSx4nZCTcnW3JBxEpMMMbQlLNpS7IIPqjnTVhoiBkUOxCQr5mYA20JO2dov2breRJmM2604MhZiRMnmdSfKBNxGWxeLthS74Jm2+v+lTqdOgMmk/rx7d1rC4EFMdq4zUSbjNisX9gfbwC+b2yGb1z2YrO0ga1zpIMO683xDla3rHY7tEMC2IEovFm0pdsFfROafaavXA927qz8fOAA0NgoIjIhkJMNBnagYZFh3GcjSDrLEQeLJmAsyxpTN2B9EREQkCxZtKXbBB7H+oi0QKNoCwN696YtHAB7Ik6yYm9QS5gjJhPlIRLHgWCEXGfpDhhhE4OzvUKLXX/TyKXuwaEux83q1H5Xgom3PnoGf+WVkWSmbZzPyACogm9edqCncLiicrDkha1xERESy4z6UWguLthS74IFIH5Q6wV9GtmtX+uKRgAxfAsYdRADbgogoOtHjo+jlU2bI5jyRYd1liIEoE3BbCWBbyIX1ibaHRVuKXdg9bbWNsXt3wGxWf968ObS4mwYcFNKPbS4/9hHJmAMyxpRubAO5sD/EYx/IS5YrqbIxR7JxnZvCtpCLrP0ha1ytLVvXO5uwaEuxCyrahtzT1mQC+vRRf66vB/bsSW9cRET/I8uBC084SXbMSSKVrNuCrHFlC9n247IU0LMV24GIRGHRlmIXtLNS9GGp079/4Odff23lMLjTJCKi2Ml28k0sQMiAbREd24WIiFrCfQWli5RF27KyMvTt21f7N2rUKADA1q1bcckll2Do0KGYN29eyIbSGs9RmKZm2gLA0UcHfv7xx/TEQyQhFiICZIhJhhiyGdufqGXcTkKxPYioOdk6RvADaKLsJGXR9ueff8bixYuxceNGbNy4EW+99RZcLhcmTJiAAQMGYOXKlSgvL8eqVasAoFWeo0hKc0XbQw4BunZVf96+HThwIH2BpZEMO0cZYiCKhrkpN/aPGLK0uyxxkLx9IcOXl5BY7A9qCXOEZCFbLsoWD7Ud0hVtPR4Ptm7diiFDhqC4uBjFxcUoLCzE+vXrYbVaMW3aNPTq1QuTJ0/GihUrAKBVnqMowr6ILIROB5xwQuD3r79OU1BiyTA4Z3MMMnziLEP7E1Hm4JhBRNFwbCDKHNxeA9gWYslwPhxOhhjaEqPoAMJt2bIFiqJgzJgxqKysxNChQ1FWVobNmzdj0KBByMvLAwD07dsX5eXlANAqz8VLUZSMSk5/vHHFHFa09fl8oa8fMgR4+23oFAXKV18B55wDhN/7NgXC405n24tcdmvEkFAepDiGZMgQhwwxRFuuz+eDLvzDlQTeL551kaEtZIhBljhSHUOm5oToONpiGySSC+HHDG2hHTI5htaIIxVjhP+xdGqr/ZGKGCKO9RN8v3jfI3y5Pp8P+lY4p4g3hnT2R1scM1OVDzJsG9kaQ2vEkarz0XRvo221PxKR6m000ZzINLGun3RF2/LycvTp0wcPPPAA2rdvj1mzZuHBBx/EkUceiR49emh/p9PpoNfrYbFYYLVaU/5cSUlJXHHX19en/WAiGYqiwG63A0DshR2bDTn/+9Hl8cBms8FisYT8ScGRR8K0bRt0NTVo+PFHeA87LIVRq+rr67XYAUCv12uF99Zms9ng8XhCYjGZTGlZtp/P54tY//B+iFVCeQC1HYJjAJBwDMmwWq0hcZjNZpjN5rTGYLPZ4HQ6td/r6+vhcrnSGoM/jmAWiwUGgyHh90skN9xud0h/uN3utOdF+PhgMBiQm5ub1hiAyNysr69Peww2mw1er1f73WKxJDVeJZITHo8nZeNVMux2e0gcPp8vbXFYrVY0NjZqv9fX14fsR9IllfmQSC64XK6QPvB4PEJyQeQxhJ8M4wOg5oTb7Q6JI3h/Fq9E8iL4NX4WiyWpDx3jFX5MoyiKkNxsaGiIOKZJ9zGmzWaDw+HQfk92vEr0ONNut0eMV+k+zwo/pjEYDGnNi2jHuOnOh2hxNDQ0wBd8y7w4JJMPwcUNGfLBaDSmfZwIHyNEHVOFx9HQ0JBUfyRzPhp+DpbO4yuv1xvSDjqdTopjXK/Xm/Y4Uj1eJZoTmSbWsVS6ou2FF16ICy+8UPt9+vTpGDlyJA4//PCIYkxOTg4cDgcMBkPKn4u3aFtcXJxUgSTd/Du+kpKS2E+4ggofppwcFBQURLbTsGHAtm0AgMKffwaOPTYl8Qbz+XwhJ8CFhYVx91eiCgoKQk5wiouL014k9Pl8yM/P1343GAwJr38ieQCoBwkNDQ3a71FzIQ1cLldIgTSdueBXV1cXsu0XFxenvQAAqH0QfEBbUlKSdNHW/z7xFGWCc9NsNqe9PzweT8gBXFFRkZDcDD/hFBFHfn5+SAwlJSVJH0D53yeeom1wTuj1eiH9YTQaQwpjeXl5aYujoaEhpL2Ki4tRUFCQlmUHKygoCDmZSWb/lUguOJ1O4eODX/CJhYj9ht1uDzlQLy4uFtIW1dXVEcc0yXzIlUheKIoSkhfxvj4VDAaDFMc04fsvEbkZXgxLdrxK9Dgz2v4r3UW68GMao9GY1v5wu93C8wFAyLYBqMczRUVFCb1XMvkQfowrOh9MJlPa+0PkOXCw8H1Ysse4ieZFtHOwdB5fyXqMm5ubm/Y4wscrUTmRaYL3c82Rrmgbrri4GD6fDx07dsS2/xUD/Ww2mzZgpvq5eOl0uoxLKH/MscatC56+rddHf+2gQUBuLuBwQPff/wIjRwJduqQwajXuvMpKdNi4EdbDDoMybFha2z54WaL6PZUxxJsH0ZYpsh1ExyFDDP7lhv+ebBxxjxFR/lZ0f4iIIVocbSU3Ex0vUhlDokTFIUMutEYcyY4P2ZgLzS1ThpxIRRzJjhGpiiMesvaHDLkpcr/RFtsik5bfWnFk4hjhX54s/SFDDBwnuO9o7RgSyYlME+u6SXc9/5w5c/Dee+9pv2/atAl6vR59+/bFDz/8oD2+e/duuFwulJSUYODAgSl/jqIInr7dVIKZzcCpp6o/ezzA0qVAK1wq3vHLL1G4Ywe6fPIJitatA9r4/U4oOhnucyP6Xnyil0uZgzkihiztLkscRMGYl0SUiWQYu2SIQaRsX3/KHtIVbY8++mgsWLAAGzduxJdffomysjKMHTsWJ598MhoaGrB69WoAwOLFizFs2DAYDAYMHTo05c9RFGFfRNakc84BOnZUf96xA3jxxZQXVfP27dN+LvzyS+Af/2iV4nAmkGGHJUMMgDxxZCu2P1HLuJ0Qc4CikSEvZIghGlnjykbsCyK5ybCNyhBDWyLd7RHGjBmD8vJyTJw4EQUFBRg5ciQmT54Mo9GIsrIyTJkyBfPnz4fX68XSpUsBoFWeoyiCN77mirZmM3DDDcCTTwIOB/Dzz8A33wBDhqQsDl34QPDFF8BvvwG33AIcckhqlhNTKByQiIhixTEze7HviTJPNl89JMuVVMS2l50s/SNLHOkmw3rLEENbJl3RFgCmTJmCKVOmRDw+cuRIfPDBB9i0aRMGDx6M0tLSVn2OwoTdHqHZjbNnT+Daa4Hnn1d/f/tt4Jhj1IJuknR2O3TBs34NBui8XqCqCvjb34CJE9XlU6vhwCw/9hERNYdjhIrtQEQy4tgkF/YHEYkiZdG2OZ07d0bnzp3T9hwFCdpZKbF8Y+fAgUD//sAvvwC1tcDHHwPnnpt0GLqgbzG1HH00nMOHo9OaNcD+/UBDgzrD97rrgD/8IellyYgHDRSOOREgS1vIMENHlrYgiiabZ/DJgm2hkmG8jkaWOEgOzAeSRUblosOh1iFMJqBDh+avFo5TRrUDZbSMK9qSQLF8EVm4sWOBzZvV1374oXqLhCRvXxBctPXk58PTsSNw113qrN7ff1cH5+efB0aPBs46C4ilwEyUItyBB7AtSJYcEFmUkaUNSF7MkVBsDyJ5yLg9yhhTOsj0AZPBbkfh779D5/UiJz9fvZq2a1fgqKPUAqlOp/6rrwf27AHy8oBevVJ+Xp63Zw/af/cdcg8cgPatRD6fOtnM6w39zpu8PKBvX2D8eKCoKGUxGK1WmCwWuIuL1eWmsDBMBLBoS/GI9YvIgnXtCgwfDvz73+qg+fzzwJQp6qCZIL3Vqv3sKShQfygoAG67DXjtNeD779UBc+1aYMsW4IQTgJwc4OijgdzchJcLyHmQIGNMBHV7qaxUD1zcbvVTXocD2L0baGwEDj8c+OMfU3bwovN40O3995G3dy8UvR76Xr2A9u3VZR1yiLotdumiLk+vB2w2oFMnoF27lCw/PBadxwMUFqb8vWPm9bZ8GxcRvF6grk4dA/PzxcXhdgP79ql5WVenPtaxI9C5M1BcnJJb2URQlJR/KWVK1NerV2rYbKH/3G51Gz3yyNQuz+FQb+djMAAeD1BSAhiN6phhMqlt73Sq92nfsUPNmVNOUbfhVFAUGBobgYMH1THAZErN+1LGk+qKAEUB7HY1P/0FABLH51P7w2ZT/zca1bFs/351vOreHejXj/1EKSfdcRwBAHq8/TZyDx6M/qROp54DFBcDe/cGjv0uvFCdUJUiersdPVevDrltYrMaG9U6QY8e6henp0BOVRV6v/46tJHv//5PPb5v314dG7t2VesP3bur4+a+fer5YbduwEUXpXbM9Pmgd7mEHtfpXC7kHjwIndGY1u8ZautYtKXYJTLTFgAuuEAtnu7fr/579lm1wJpgAVUfPNO2oCAwSPq/AO2DD9SCraIA27ap/wBgwAD1i8qSVLhtG3Kqq+Hs0AG63Fy1IFxa2jpFjgwh8jJXc3U1OmzciNwDB6ArKACOOELti99/V3fOubmBfy4XYLGoOTlgQMpiaP/ttyj6/Xcoej1yTCa1COZ0qstvymefqbGMHJmSOAp//x2F27cHHtiypeUXGY3A9Onq5UIpoCgKcvfvR8+VK6H3+eAqLVVvkZKbqxaH8vLU7cVkUtvGaFRvaXLooWqxMEVKfvoJndavh97rhc9kUg+MOndWt9OcHGDnTsBqVR/r2VM98XQ41AJdqgpj/4uj/Y8/wmcwwOzxqGOSxRIYSzt3Vtf9kEPU53r3Vj9cSuEBXLvvv0dBRQXg88FQXKy2eV2d+uGBxxP9Rbm5wJ13qge1KWCw29UPFPbtUw+sTabQ7TIvT/13yCHqtuv1qjMhjjoqJcv307ndKN66FXqXCzn+7fPAAfXguSnffgvMmqW2WxI6btiAkl9+gd7jgb6pdm/O7t3qFSVJUDwedHv3XRTs3Am9Pwd1OvWDm44d1f42GNRtsqRE/deli9oXKRB1P+GfDWMwRD7XhklVhFAUGK1W6F0u6L1etS9KS9V/Op163FZeDmzfrhbrunVTT3ZTecyjKCj56ScU7N4NU12dmp8Oh7o8QI0jJycwPrRrBxx3HHD22ekpElqt6oc7er267CQnAKSE/4Nht1stBqRoG1IUBQabDYXbt8NcW4vcxkagulr911Lejh0LnHlmyuLI37kTOTU1cLVrB6V9e3UfEdzfdru6HzMY1J/btUtpsUKq7VQwg9WK4t9+g8Fmg7lLF/VYZs8edXs0GNR//uMLi0Xti2uvTel3jOhdLuQcPAi90wldXZ16TO9wqPvyxkb1Z69XPaYtLVV/7ty51T5MEJkfOrcb+Xv2qDNcAbU46j+uOXhQHbfr69X9eUODOk6cfjpw/PGpWb7D0XTBFlDHCv+yg/30U0qLtqYDB0IKtr527aA3GtX+1usDxePSUrU9Nm9W/7CqKmUxFP7+O0Kyy+VS//knROzdG/2Fv/wC9OmTsvPRvD170O2992B0OOAzGtWZxF6vOiYajYF/BkNg2zj1VPU8JEVKfvoJnT77LHCcq9er53/FxWo/9Oql5um2bWoflJaq511nnJHS86+2iEVbil2897T1y80FbrpJ/ZIwq1WdObRoEXDzzQnNNAsu2nrz80OTWKdTD+J79waWLg0MmACwdWvSlyyYamrQ/f33Aw/4f9bp1IGnQwd1HQF1x+n1quuYn68OnkcdBQwb1vZnNrlc6gFCfn6rn1B1/eCDwIFDXZ16ENmS995L2U7SWFeHThs2JPbi339PSQwAYLJY4n+RxwP8+qs6iy9Fijdv1gpC5poa4PPPW35RYSEwY0bKToTbf/+9WnwAoHe71SLtzp2Rf/jbb6G/f/edWsROBa9XKxw3qbIysmA4enRK7v0NADn796PzZ5/F/0KHA9i4MWVF2+LNm5EfvF263eq/8IP5YB9+qOZECr8ctPTbb9Hx66/je5HNps5GTmK2gKGhAR2+/Tbh1wMAKiqS3n/l79qFouAPdgD1PWtr1X/+DzjD3XWX+mFYChhsNnRevx4mi0UdJxoa1P1FUZE6KyUnRz3hz8lRD/QbG9V1PvZY4KSTUhIDABgaG1G8ebN6Wad/BkynTupVOwcPqjNhnE61/w8cUOM44wxg8OCUxZC/axfa/fwzjA0NyLFa1eJG+/ZATY16YuXfTnQ6tSCl06n7rVGj1DhTwetFrzfeQF60E1j/VRnhHzL89JO6XaZwv1GwYwe6fPpp03+gKIEP1wC1X/bsUU94U5Sb5poaHLJhA/QuF8w2m3r8lpOjLtNmC/3jzp3V/dYppwBDh6Zk+cD/ikA+H/L27oXe7Yb54EE1Bzp1UvvB41EL119+Caxbp+YKAJx2GnDppSmJQed249B//APG5j50bsrWrSkr2uYcOICea9YEHli7Vj12zs9XP+RTlMj9Z14ecP/9rXIFERBUpFMUdUxwu9UPt1rxcuiSTZvQ8euv4SopgXLooerycnLUdczJUbcDi0Vtl4KCwL/SUvXDuJyc5IPw+dB7xQqYmttnh9u/H/jkE+Caa5JfPtTLzw9dtgyG4EvdY3XrreoH4ilirq6GubZWvRT/4EF1rPB41PEyJyeQl+3aqX3Rp09KL8Uv2L4dXT/6CAb/eBir119Xb1OYgiv8DPX12s+2Hj3gO/FEFBUUqMfVe/ao20ZVlfp/586BNvGPWSliDIqjcvhwFIwejcKmrvJzOIC77055HOagGNwFBTAVFanvH0uu7t+fsvPRdps2wfi/nNB7POpxXXN27lT357NmpaQuoSgKOv7nP6ETE3y+wKQ9QJ0IEay+Xq0L7dsX6BuKikVbil2iM20B9YDzttuAp55STz7Ky4EHHlA/VcnJieufcdcu7W09BQUwRPuks18/4MEH1ZPQRYvUx9xutaCaxI7T3NSniooSmIkQLmgwxw8/qDuxFBxc51RWwmyxqIPjpk3qgOc/cCssVP8VFKgHllaremJz+ulJL9dP73Kh/fffI2/PHujNZnVZ/pN//wmO/5JGfx+VlqoxDB+emiB8PuQ0t+M1GqPPJjxwIDXLh1rIj+A/YOjWLTC7tF27QPH+739Xn4+WL4nGEXTbkJ2XXYbu/fvDaLerhdCDB9VPeisr1YO1n38OfKCR4gMoU3C+x8pqVeM7/PDkA1AU7cTCZzTCV1wMY6zrWFmpjk8puG2BwWIJKdj68vLUT/9LStQPd/wfMITn55YtqSvaNjcL4pBDgMMOU8fmkhJ1O/3uOzU3gNQe0AZ9eKbo9dB17hwowDgc0Wdv+XxqoTKFRdu8aLMdTCZ1NumRR6rt4D/x/fZb4Jtv1L+prk6qaBucf578fOg6doShRw91fNLr1fFIp1OLMf4PvPR6dfz4+GP1hS6XOq4mccsRc9ABfGOnTjB36QKDxaKOD+FFqWBbt6asMNb+++9RFP5hCRB9Rk5FReDnn39WY+jUKSVxHLJhA0p+/TW+F73xhjq7MxXFGa8X3d5/HwanM/CYfwZQc/z7+WuvTT4GALl790Yv2ALqNtjUJadNzRxKNI6gGBS9HsjPh06nU48RFUUtoLtc6v/BJ6L79qUsNzts3IjCHTtCHwzar4bwf+BWUaHmRJIz8YN1+fhjlMSSC8F++CFlRVvTwYORBVuzObCv8E9E8HjU9e7cWd02gJQe00Qdr91uNf+b+pC6sRH48Ud19liK6Bsb0WXdOhitVvhyc9VtoqoqUJAxmdSiXc+eapHU7Va/CHnYsKSXrSgKOvz3vzDa7erx3L598b/JsGHAlVcmFYehvj56wdZ/z9KmxokU5kPBzp2JFWwB9ZwzBUVbRVFQsH07eqxdG98Li4rUD6FTUUAH0OHrr+Mv2ALq+FlXl5LjKkPQNtjYrRt0Aweqx7bBH2K53Wpu5OQAjz6qFgktFvXxFE1eCi7auouLm5/9nJurjl12e8sFzRgpihJy3rP96qvRZ8AA9RZxNpu6vKoq9efycvV3RQFWrlRfkKJj7fA4PEVFMOp0gdtweTxqu4efc9jt6nbapUvSMegcjpB9h6t3b5j9t2KL1i85OWpOAur5EO8F3CwWbSl2Yfe0jfuykB49gEmTgKefVgcJlyv67LcWBCetJz8fTe4CzWb106thw4AvvlAfq61NqmgbfNDScPjhyG/fHga9Xh2QKyvVAoTBELiUz2QKrKtfkrMrFUVB3t696OUf8GP13XfqyU0KLlVSFAWHbNiAdj/91Pwfut2hv1dWAm++qV6GnoLCmN5q1S6LsfbuDftll6FTfb3aB127qrOWvF51p+BwAE8+qe6c7Hb19xTM7DQGHbjsP/10FJ1xBgpaOiB67z01jhQWxoxBuekuKoJSUqKeQADqyVb//oE/rqwEysrUn1N4UA0EirY+oxF7xo9Hz+Ji9YmamkA/uN1qMfv99wMHEMGz4pNht2uf8jZ264b6q69G16IitSC0c6e6vEMPVQ9ad+5UDyZ+/DEw67amJjVF26D1qTnuOGDsWJSG54XbrX7CbLUCL76oPpaiA0kgNCf2jRqF0mHDkGM0qusX7eThj38EJk9WD5xSmJvB42b59dfjyGOPDTypKOr4aLWqebl1K/DRR+pzqf5AISiOqosuQqchQ9QiRLRZJxZLoGibZBzGoD6tPv545J99Nopi3Q81Nobuv5Io2gbPxq869VR0PvFEGPxjYH29WoDyz2T75ht1tjOgFnVTJCdovPEZjdD7L5mrr1fbXFHU3HS5Ig/y9+xJSdFWURTkJVL8sFqTLpz7GerrQwq2isEAXXOz8v0FfQAI+uA6WcH7L3u3bjAdcQRMOTnqfsE/g6xjR/XY4ZBDgMWL1T9O9X4jaNvcedll6HnSSTA0dbn/L78ACxemNA5FUdQrQ/y/6/XQtW+vtrnBoOZdaal6HLxvX+ADBf8lsP59bQpEFI6bEvyBuMUSKKImKbgIUjtwIEyjR6Owe/fmT6b//W91/K6pSdmJd/B4VX/UUSg0maCvrg5cCu92qx9slZaqVwz5jyVSvN9ov2kTipo7bvcf5wZf0bNpk3plXbJ54fXC2NQHB7H64gv1g+D27RN+i+CccLZvD+9ppyG/Vy/1qsacnMAXPnm9ag7ef786XqbyOCIoHyxHH43Cvn1hKCgI3F7Jf7slnU69nVBVlTo7G0jpcVXM22ewhgY1plRdFRDUFg3DhqFIUdR9qNGoXoael6f2Tbt26jnvRx8B69erLzh4MOVFW3dREaLeLCe4MNuhg7p9KIo6ZqboXqfGsEJli9q3DxRtfb6UzDr2n/d48vOh+NfZZArM+Pdftea/1VRtbcqLtsFxuAsKsPumm3DYYYdF/tH/rubAmjWBSQEpKtoagrazuv794Ro3Dp06dVLP/fz7TosFMBrhLi2Fo6gIhcuWQffzz+o4Wl+vHpNTVCzaUuyCT6ASPSDr2ROYOhVYtUqdVeJ0JvzFNF6zOTA4Nif4QKW2Vr2fSoKCiyC1xx0H00knBU56FUU9ocvLi7y3mNcL3HGH+nMKBuiCpordTc0s9du3L2X3l8pp6nLKkhK1zU0mdQD2fxLvvzzaP0shBffQCb48x11SAiU3Vz1QCfkjQ2BmyJFHBk7yamrUA/4kBR8wuPyX97akQwc1Dv/JRxJfzAf87xPW/x3Y+wwGeFt6v+ADtlSe4ATNcnUXFcHVqVPzB6kdOwKvvJLSOEIOJP3FFf/M8/D7JR19tPrP/6VPgNovKbgtQEgcJSWIOlKZTOplc4B6efaePa1yIAkAjkMOUQ/mm8tPk0n9G4ulVT7995lM8IZ/UOL/gCsnR90uTKZWKdoqXq82fjs7dIC9T5/mT2KD7/OcbNE2qIDvKilBXB8JhG+rSYzfwSe9bv+HKX7FxaGX6B1ySKsUbf0nmz6jEdtvuw1HBH/Jm/8Dtrw8tRi2e7e6Xb7zjvp8qgqFPl/IuF198cXo4POpJ5INDWou9umjzrjOzVXvP75xo/rHBw6kpGgbXCytPeYY4JJL0N4/C6Zdu0DxIzdXPbYwGoGHHgpc0dMKY0TN4MFoP3w4TE3desHnCxxjpPoS1+APHVuaLRW8babyahX/FRomEypuuw29m9t3rVypXvoNqG2RoqKtzuHQZtC5SkrgGjQIhQ6HWhA1GtVtw+FQc3PUKPWk+7vvAoWQFMQRvG00dusGo/9KjOaUlqox+j+AS8Gl4KagcfPAyScjf9Ag9WoVv+Di8IEDwMyZ6s8p/kDBHJ7rOp06PvqLP5WV6rYRfOytKOps9CT7w2CxaPfKtPbqBfdJJ6F9hw5qDtTVqYWO4mK14OK/jYfdro5jmzapYyigtk8SRdvg45m6gQORO3RoaHFFpwvcLxNQl2W1qjH673mbpOCxqvqPf0TBkCFNf0jhv7VOKxRtg+M4ePLJ6Oi/asZgUPO/vl49njOZ1HNd/2Xh1dUpKdrqGhu1D/xsPXqg/rTTUNS9e/MvCv7OiBRtH8E54WmqaBss+FgmyauXgoXvO1pUWqoea/t86rFuEtsFAMDlgvF/VyrFtHwgMFnA50vdvtTl0ma5NhuHf/ZtcJE2Rcd4wZNVQmLwn3cEXU25Y9s2+KxWHFpYGJh8V13Nom0zWLSl2AXPtE3mZKG0FLjxxsB7ut3qiVos/xwOeH7+GcaDB9HgL3a0JLxom4Tw2Ywh/Dc7j8ZgUAuGv/2mHlQlOcsz+GC2duBAtD/tNLUAWVSktqfNph4wWa3qQdvbb6t/3Aqz53xGI/bdcAO69+jR9Kw1QD25eOst9ecUFW31wTuIWE4Swg8aUlC0Db8cJaYZ6ME5WVOjHuAlyZ+bnsLClk+w/MW5+vrUnuBYrdos1xZPvIHQdkjRTFt92IFkTFJYoPMzhs1CaPHjpVQfSCLssrFY26K0VF2+/4srkr2ETVG03HQXFbWcmykcr4PpbDbtXsvuWIpu4WNFEoKLtu54D0jDx4okmIIKpt6WZpPn5ATGiFTdTsbnC8wEiVYI8n/A5l/+EUeE/k2Kxip9Q4OWCw2HHQZH377Nj8G//x4o2h48qN5WJEnBJ7yu0lKYdLqWZ7p06aK2gcej9ksK7tsZfmlps/R6dbuoqkrpjEogsB/1ms3wtfTBZ2t86BhUCGns3LnlGautVDgOPqZp7NYNjtNOQ2ETX9JpsVigGAxoFxxHiou2ce03/GpqUlO0DRqvPNE+SAjOvfbtAzOPU/lhn6JocSgAtt94Iw4/+ujIDz8VRR0nN20KHOemYNwMPp5xdegA91FHxf6lrYWF6pVtgDpuJfHFnsETJDyx7j/992G3WJKe2RneD55Yji/9s28djtQWbf15aTKhbsgQdGzuXNTjCVydkKLCmC5ojIj5nCN4XEhVgS6efQfQKsfZQGC88ppM8MXy5ZjBuVhbm/yxdlBuxVy01evV5abwikt9WF60KCgnPPv3Q3G7YUryeD94pq27pCT67Sv/x/e/YzBHQUGgaFtTk5rb5LVRLNpS7FIx0zac/6btcdznx3L66ajbtQveWGe7pLJo+7+dg6LTRT+IbE74QW0SBUP/bCVFp0PV8OFo369f0JNm9Z9/vQsLU1+0DfpEz9GpEzzFxS3v+II/VU1RESD8k159SwcvrXCyZwzqi5iKQUDkCV+yRdvGRu1eXzGfZHXooJ7419erswNS8U3gQSevMX/i7dcaM21jPaBNYYFOi6OpT5ybEj5OpaBo6/9gxZubCyXW/i0tVb8lHlD7JNYTxKY0NGj39vXnpqIo6v0qo2mlk299vP2Rwtz0f8jmHyPiurVQquIIml0atWAaTceOgTHC6Uz+fny1tdrtbFyxFq+DTzZTVbQNaseYiujB+67WmJESy4c6QOR+IwVFW1O8BTp/0db/JW0pmHWseL3alSIxxWAyqR8QWyypy4l4T7xbqQARTxz79+9Hu+Dj0Fb6wDEm4bkZfsVTvHw+rTjmH6+aHTf994uvq0v9TNv/xeEpKoKnqVsL6XTq7M7gmZQpOM4NP56JSwqPuQ3NTViJJny/lYLL8bVL0AsLocQ6c7d9e/UKw9ra1HzI5PMFriYrLkaLe/LWKJbGO1mlleIIHidiLuT7BW+jPl/i/eLzBSasFP8/e/cdH1lZ7w/8c6b3STLpbbOFXWDZXdrSFAUpVlT0qtj9qQiXq+IFCyByueIVUFGuiCJgowlSXC9iA0WwgNIFli1sS7LpyWR6n/P7Y+Y5OXMyNZlkJsnn/Xrta5OZycyZmeec85zv832+j6u859EOhM8zSCip+xPlBm2BzOdRxRmXUqXnMFWbiAwOYmjvXmwQ5RvmSHv+KmcvTau/jyofu5cbBm2pfOp6a7UsFK3TIelwZC76K81qnGfQ1qjOZqw021jbqZ1r0FaWlYV9Ei5X6WlHVczUEirOcAUWJGir03RoS4YUFjCjMulwlD8FrNrBSlW7LqvzJLZBBOe83vkH5zCHC1+Xa2aKUJUyIbRtoqyT3AK0i5yLrbkECec72qyqhacOlla8HfNsFxV3JPX6mYvvKgZD1NtRVhaC2ZwJSM23Lp8sK5m2Caez8mmi1TpW+HwzmcbZ76Fke2hpmanBPjEx78ElST2oU27A0enMBOkSidoF6NQXvAsw4Fj2hZ62D1GFqbbK+ctqLW9gR9seqxC0hd+vBPOTlXwWYkZAFQYdK85SWqASQ3pN2yzVy8zpf1Vp/xCDO2mdDqlykxMKfR7ZAR8pHM6dQq/TZY6FhfrR09PK8SpeSZuYns4cs6sxyAQA4bCSgV3WQFOVjxXawd+K8uCqONhUcd3Qau8fkQgM2bIhFQXGRNA2maxO2Q6fb2bgsdLBnWqdvyodgAYy34e4Zi5nO2R5ZtEqsTiq5p84ViXtdsh6fen+hPqz+P3vER8agtHngzQ4qJR+cQEzxwWxPoxOl/knpvWLn3U6IBhUasEny+3nqtvmz36W6eNYLJn9NZHIvHdR91X8rP1d9bO65nTFbVOowozLnL52OQkrbrdS7sikXkA6nc5kp4vShup/4nitvk1Fm2lbaD6xetvS6r5glcsuLTcM2lL5VDtZOeURUqkURkdH4XK54KhGx77gZpU4MKkPCPOZgh2LKbXGREblnDOm5nHylv1+6LILH4gsoaKZa3b7zCIm1QqMzWU6SHPzTKehWhe+miyhkl109XfQ359Z9Einy2yb05l7gs530tbeHo/PtIlKOoPq7bjvvkwJC7FInqiJrL6oyfe/+KfTQVKtgj6nkgBXXQUcd1wmOBIMZoKpkgRrNJo5qYtVxFOpmQUnxG2qf6bdu5WnLOvzEAE6r7d6bVO7emo5f+R2Z7Yllcrsm2LxmXQ6Ux9O+15FG9D+rPpdLD6VtNkgl7M4jHbK1nz5fJCyx6c5t839+zNTsh2OOZdJmFMWQlPTzMV3IDDTkevunnNtvDkNNDU1zdTl+9a3Mrep939tGyjQFnQiC77IRX8sFoNOp5s9Pa2hYea4uXMn8D//k7mQUneY8/2vvV+1KGTZJRrUAYjrrwfe9rbMhUb2IsoQiWSOFaKeX4l/OtVxP+52lzx/yrKMYDAIR1MTpCoucqTTtslS5/EFzlKaU7ZUNQIAyaRywVlRsFS9DfNYI0Axl2OExzMzoDA5ObteeYVmtYlSFipoqwnIlOrT5GxrNbZDlpUAXTJbzqasfq66XTz2GPDss5nz6MgIJAAFjzgiEKP9p/4cyh3g8Xgyq7MDmc9inm0C0Aw0lXPcFAtjRSLVKY+g6c9UdBZuapoZFJ9vpq1qgdmUxVJZ6atqtMtKZ0cU2o55Bm0rbg9mc+Y1A4EFqxtaVvqQWBTL68306266KbN/xmIzQVnxfzI5exHpfNshtmEuJVQAmF58ceaXeBxSPI75nNkr6l8KsjyzQNscqbc5Xs41eb7t+N3vMiUUGxtn+pHqbVT/rP09y/znPys/l/VZ6HSZY+boKMyTk+i9917IFgukkZFM0LYcmqCuOVseL5Vv/QqVpKr+d0p9fBf1+sVsniosjracMGhL5VMfRMroyI2OjiIQCCAQCMw75V5NvK5UbmfSZJrJmNq7NzOylq8erfp/7c9A5iCSVXZgTK1K2XyS6sQfL6czK0mZE8HoaNWmCFWctQZkOg2NjZn3fuAA8PzzmQ6NxZK5r9g2FfheLNkgoazTla7RCOQGQHbvzvyrkopGWNVtAQCefHJer63+5MoebdZuwz//Oes555OnUvbn0diYaZfBIOD3I7lzJzA5CYPJlH+kV4z2qvdZ1e+Wl19WnrpQ1vH09DRCoRA6OjqgE6P4jY2ZTvXBg8BFF+UGq+dA6dCWm9WoDZZu35650Bofz3RexMrMqVSmUy0WPlHfrv6nOk6UHZDRbsdDD2X+AZkLUYMBLlnOHW0v9H/2Z6P6IqfcY4X6QuvSS2d+Npsz9URttsyFvcjayvez5jbzY49Vvh3NzZnBHVnOfCfzFC8wRTSZTGJ/9vlnnSvVmcfRaCZraJ7KDoKog4SRyEx9RGSOD5UOxapzIcu54I1EIhgaGkKvwwHr6GjVVhfWqwIYBRcJVFPX9927F/judzOrQHd1zbTVcoLoqttM2e8xabWWt6AqkHvMPnAAeOmlmfIV6plQasX6NNGocu6Y0wXvj34EnHJK5rVFtlE6DXs8njlGiAE+9f/awT9ZhkEs1IM5Tsf/n//JHBMMhkxfQpTcEv8slsK36fVAKATLb34zsw3l1ssUwbkFCtrGXS6YSmxHzue1ezdw550zwRhgJkst3z+Ryab+X5ZnBpmybSIWi6G/vx8tLS2wFprCq/4+KhmEleXMeazIArplB+nUbfPRR4Hjj8+UaShn0LQAXaVBOrFIWX9/pl2Mj88kLMzl9TWDjZZKEkX0+sxnMjGRu1iaus+UJ1tuFllWBpkS5ayZAOR+F7/+NfCHP2SO27EYkExm+hEVfCaS6vg252zGHTsywdOWlkwpizl8J3MahPZ4Mq/r82UGFWKxzLnM6czNIlX/rP6ONP/r1dvgdMJQ6NifbzvEfvnSS+W+5ZKi5c4aNZmAI46Y/drNzYBeDzkWg5xKQUL2+9YOgouB8ELbkc1ULXns7unJDOjMty+V/T5kSYIMINTXh0glM2jV/avnnsv8m8/mqH6u6Dw6OgoAsKrOw2XTBpHF65cowZVStdmU2TyTWLZjB/DFL84EjR0OSJ/9LBcny2LQlsomqXZMWbUzBgIBRKNRNDc354wsBVVTBmquuXnmokIsKDIPFdeWAnI7tX/6U6aDnS/LoETWkuEf/1CepqygLTATtI3Hq1KHbk5Za0CmsyQ6HLfeOq9tUBMZISUZDJnOWvYkVU2xPCuhFhxtbWrKTH8XmUJVIksSwt3d5T148+ZMQE49LaZKYk1NiDU3l1XPKKdTfdllVTspJW22vHXPZFnGaPb7DwaDcImOd2trbiZEkYvISkTLHSlWX+S88ELmX5VUlJnS2TmTnaMWiWQ60/PYjrh2oKCQQvXvYrFMp24OtAMbJetfA8Cpp2Yuvr3emaBYvgsq7c+ai6xEKoW404mpI4/M+zIJVVZLKpWCXttut24FHn4487PBkOngiu1X/6/uQGtvR6btx5qbEcgGhkte3Bx++MyAZxXJkoRYGTWbxeeScDqhhIq+/OVMANViyZ0Sn+99a+/L/mxWHf/LnnLc3j5zrNy1K/NvHrTB0oqzGV96qaoX3mUP7GjPc6rsHiDzvuazlEnZ26GdShoOz+NVc5Vbiw8eT2aWzNRUJoA9NZXZV0QfDsgfZNDepvrdlA0SpvX6oqUJxEWvbDIhZbFkZvx4vcATT5Sz5WUR34Xf70cymYTP5ysctHU6M8eL7dtzbo43NSHV3g49AKNOl5laXmigUfVPTiYhhUJI2O0IrFtX3gar94+//z3zz2icSQwAih4f8/1vzAawgQrqcIugLQD893/PLgeRb/ZUgZ/N2cG8shboy6e5OdOvSSSAz342/2OKDYxLEhCNKufLOc3kAjLXHtnBsrn0I3KyGbPnjooX/X3wwZmfXa7MQE+B2TGFZtIY5xI8bm6eGfT9znfK+5sixFlPliQkHQ6ljIhaOBxGOByGx+OZuQY5+eTMdqj7tpKU2TfU/0ymzP+ir6G5P6nTIRSLIY5MYkSokgXuzj0XeO45+J99FoGuLoRWrcL6I47IviEZfp8P7mIBP/Vsx+yx5ODTTyOVTEJeswaIRhFX7bN56fXAJZdkjt3hcGbwLhDIlAKz22e3/0I/Z8VjMezfvz+TAFJJksfmzZnz2MGD5f9NGZJ2e/n9mvXrZx2zYbdnAtsiwanUzFPVv5TXC10oBF/2Oy20j6ozbdOynLn+GhzM3KDK8pWCQUh+f1VmTSwHDNpS+TSZtsLQ0BAAwGazwZ7tZKbT6cpKByy0N785kyU0OVl6KmQZYq2tlf9RQ8PMFGwgs7LqHOR0XsoN2qqDIPfckznxJBKA2QxbMpk5aRUY1c33v+Uvf1GeruwLHCCTJbdzZ7mPLlvZgUoAOOcc4I9/zHQi29sz38noaCYgVKrzmudfMBxG1OWadZIS2SlNTU3waDuwkgRceGEmWyoQyHw/jY2ZC75EIvcCRlzkaP9X/ZxOJjHq8yHS0YFkQ0N5bdzpBL72tZmLTJ8v8xlkgzQygGA4DIfLBSnfBUaejJ1IJILhV15Rai2XdQyYy75UBt/mzXlvVwfIcrbvzW+emY5vt2d+1usznap8tbXyBehUv/tDIUSMRvgPO6y8DXY6Z7KO50JdNiN7kZiORBBvaIA/z0yHWCwGk8k0e1ChsRH4xCcyo/6xmFI3DH4/5FQKcjoNSZJmjkMlLn5lWUbU4UDgsMMKZprOsmHDTJDSbkfq8MORjsVg3Lcv8/3MQ6ypCUm7PSfrs6DVq4Err8z8LN7THLJzDu7fj1gshkJ12NOqc2sikZgdtH3HOzIXXHr9THbOHOzKHnvNZjOSsRji8ThsxWYo2O2Z0ikTE5mMwuHhTFtIpyGnUoiFwzAbDGUHYlKJBOKxGALr1yPlcJQ8b4jAVKSlBTmXxyK7dJ5iHk95pUsA4K1vzewPU1PzboNq8UoWHLRaM8GQBVisI7R6NYAyAiG9vcAxx2Smv8+1L6U5f8gApGgUscZGhLILWJXcjs2bM9/J7t2ZNimm9CYSM5lsc5C0WpG023PaZiqVQjqdnl26pKUlc6Epy/POkhKUYH4FmUrBdevgrmIAXxBtQlxgJ4sNZEoS8O//ntk/zGbAZoNvchIjU1OQZRnpdBrr16+Hodz9TZax66WXMlno5R5zN2yYydgSRJuYo5z+drnnr+5u4JlnZn6vwgBwRQOv2m0pNdBZIFsun7IHXu124IwzMhnPySRkmy0z8Ot0QrZYkE6nodPpMp9vGa8tyzISiQSibW0IVlLLu1BN/jmeQ7Ttoaw+bnc38PTTFb9WKcnmZkCnyzkWCAPZ60uj0ZgJggKZ4/YRR2QClQZDZj+tYP9Kp9OYnJyE1+vNed969XVtoW1NJpFIJDKDPscei6nmZsTEbIBKqK/LsmJdXUgkEnCbzYiWE7TNbPT8F0ucL5stEzwOBjMzeUdHM21SXG8I2p8L3BeNRBCYnERk7dq8ZcRSqRTi8XjuwNtpp2HcakVIlhHzeNDV1QVHuUlQeUyMjGB6agqSwVB0v84pj5BKZfq4992X+Qxcrkzf2+2GvHo10iyRoGDQlsqnurAUNW1zRktU94c1WQ9l1XepkPr5RkZGYDab0VjoAmjjxsy/ZBIYGcl06opkOxTK1hl96SWkTCYEyx35V9PrgTe+Efj97zMnOLH9c7zwCXd3lx+sVHc2VRcXElBe8KIAGZmR1rKy1gDg9NMznepEIvN/NJp7gVXoeQrcHorH4fN4EM4TlAoGg9DpdLODEocckvlXJaN79iCZTM4aZR0bG0M6ncbExERO0FYp76HXz15sao4Z0HIqBf+rrwIAdNJM2RBZlpFMJmdfbCIz9ViSJFhEtoD6c2puzoya+nyZaSll7rtyMolEY2P5pUuATDBqZATYswdpkwm+ri5E2tthMpvR3NSUe0FRxkivLxjEZEsLpNZWIB6ftR3qY1NOZ3f16sy0nCrxDw4iFAoVDNTNIknAeedlLvbS6UynuqkpE9S224tn5IsAiMbubJBO2zanp6cxOjqKpqYmtOTJEMeWLZl/WuVkQmjEolH0HziQsw0lzweHHgpcdhkCXi9S7e0YzWZA93zwg7CJ8hCiNIT25wK3RYJBeBMJRNasmVuHtIrnL22bVJ9HE4kELKpaYKlUCr7sZz4rmDtHZrMZsWzQtiSjcSbLQX28kmVEfT6YK2gL0VAIg4ODZe8TYv/0rV+PVrcb0ssvz5SJiEYz5/F8FzAlbkvr9Qj09mLqhBPK2m4AwNq1SP7nf0Kv00EaG8sMug4NZWauAGVn7omfo1NTSMoyvMcdV/42AMBHPpLJIDQYMhc3bncmkC+yjsvJ7FTddnBoCDG3G2mPB0ilkEgkMDY2Bo/HM6u9RSIRAID1//0/4L3vnamTKUmZbchOE/WHQnC53TODfdp/GslEAvt27pwJIGS3LRwOY3p6Gq2trbODfXp9ZqDtzW/O/zml05m+RSw208/I93syCdjtCMfjCA8MIJTn+DA0NIRwOIxVq1bl7Js4/fTMuWtkZOYzttlmBla1+0W+9qm5Ly3LSBmNmCzRNtXnrpFTToHzzW/OZNyZTJlgjCgvJMsFa9Dn1KpX/T4yPIyIxZIJCqnOG0WDtuI9qPo6adV3Fo1G0d/fj+7ubpjKXDROuzCe9rgZj8fh9/vhcDgy30tTU2aQbceOzODn8HAmqC5qdYptFP9rv488t6VlGYlUCqFDDskEbcs5l598cmagSyySFwjk/6zz/Symhavfp8tVsD34fD4kk8nZiQHC6adn2vnevUgkk0hbLDAaDJmp1NrsuSILL8UBhFtaMHXMMbO+A4PBkDm/a73jHcBZZ2F6bAyjPh9aW1rQmP0MAxX2I9KpFPaJPq5OB7ncjMZVq4Azz8zMamtpyRynBgYyGaeynBsEzDNLRjsonwYQSyQQPOQQJNzu8jKGTz45c57w+XJLtQSDszN7C30nqv9jsRhi6TRir3lN5rPRfBbq/SShHbAQr12hUCiE0dFR5fkMBkPp40FWOp1Gf38/EolEJigoFhLPmpiYgN1uzz22VkC8X3P2famDwel0GqOjo7Db7TOz6hbInGIc4tzpdM57YdG434+p4eGCQfSxsTH4/X709PTMXBdnZ2fGstmtyXR6Xn1dGQD0+pLXgOrzVzqdRmr9evg//Wk4bbaZ0nhAps2r6v+vdAzaUvnUO6AkIZlM4qAqrV+9E2pH0FKpVPkj7BUKiYsmoHDQVjAYMqOec+TLHojyXXSWFZh+y1uAN70pc6EgLlDUWUqiVqU6AKH5F49GMRCJIOV2K9tQ6rXD69bBKqamVVHgsMNyMpVisRhkWS588rVYMh2oKvEPDyPg92faVjKpnCSi0ajSNtevX698NmK0XqfTYWJiAslkEo2NjUqGeDUVOmEdPHgQsVgMfX19VQvCFDI5OYnJyUl0dnbCqZrWlkgk0J+dvqf+fGrC5cpkdgLweb0Yy9aOliQJ7tWr8waci/EPDCARDsNmMOQNSonAA6AJ2uYxNDSESCQCq9UKm80Gq9WaPzu1iIoC2N3d8zo+lSJnM19FeYipqalZZW0WSqWv4Xc4MKxZvCORTM65tlXc50NgZKTgFDZxXNDpdIjH4xV/z/kkEglMTU0VzCiRZRljY2M57VR7kdXf3494PI5kMonW1lakUqlMhtI8ti3fxc1iEvtEKpWC1+steN5WpoDrdEiffDL0p5xSldf3Tk5iYmKioumMwWAQBw8ezAx0tLfPe4GM8YEBhMPhyqdUrl0774s7tbDZjHQ6DUO2PU1OTiptrDlbc08M8OWcM+z2zGCSlixnymeVW/tS/JnRCEmSoMtmjvn9fkyo9v3OErUCJyYm4PP50Nvbmzln6HQzdWfLkPD5MNnVlff7EAN9ExMT6FYfn1etypTrSCQy2c82W+Z8Ng/jo6OYnp7O28dMJBKQJAkGgyH33CVJSGbrsw8NDcFtNMI9j+0Im0yZjH/N95cvSBMIBBCPx+F2u2f18cU22u12xONxxONx9Pf3o6urq3CZhSLEcRnIfCcimzASiaC7uxtDQ0OIRqOwdHWhc+vWqpzXotnXKSebUGGxAO9//9xfVAToUimMjo9jOpuAoG2XyWQSI9k6lGazOf+Czw4H8L73IZFIYG+2HJckSWhoaMg7MFPI8IEDiEajOdsRyg7Cud1utBc6Hup0iGW/h1g5g4RZqVQKqVQKyWRSyRCcE0kC3v72uf1tHrFIBP39/RUdt9NGI4aOOQYWi0U5ps6Hf3wcU1NTaGhoAKanlUx2ETjPt59GIhGMjo7C7XaXvk5WSSaTGB8fhz+blWw0GtHa2gpJkjAoprOXMDY2pvRrRkdHYbPZcgLN4jplfSVlFvIQx4ZEIpE5Dlgs8Pv9yj+n05n3mKD+7KolHA4jFouhqamp5HFIzEyu9vWg9rpD9PdisVhOMtOsrNcqUF9z56NN+JuYmMD09DR8ZjNcLhesVuuczhHLHYO2VD51pm12h4yqao+od3bthedCBm3VFuLgm482EBMOhzE4OIjW1tbMibQYnS63Fp8Y3S0zOCXHYkju35+TUQkAXq8XU1NT6OnpyclkCAQCGEqlYP7kJ9Fnt2deO1vjS47FEPD74bTbMzWLi4zuqv8PBoMYS6eRbmrKTJXNdhr6+/uRTqexZs2aioNt8yG+c9ER8KqmmKvbnugciEwz8TeLFbQNhULKIEM0Gl2Q11WbzE6jHR0dzQnaarNNF2PfzCedTmN4eBhOpxMulyunYy7LMvbv3w+Px4PGbPZuOURnoNB7KnTM0kqlUghkp0GLBRWBzFQwEcS12WxlB/fS6TTGxsbgcrnmnFFQiUKdJb9mSmAoFILD4UAkEoFOp1MCerUUj8eVwLKaNqNkvoLBIGRZVr5fSZLgcDgQCATgdDrR0dExpwv/dDqNqakpTGWnBgva88bk5CSmVfXBgcz3YzKZYLfbkUwmlX3C7/cjHo8r2dsmkwlmsxkWiwVmsxlms7nsTn+hoK0sy5iamlIGKKanp5FOpyva/yo1NjYGo9GYN+CQs1hFvlq/cySeV6fT5S3lNDY2hnA4jJ6eHuU1RRmoqamp/NnpVSbLsjLw2F1kICeVSmFoaAgGgwGtra0VfUZiEAeYucgSn41oGyLQplZo9kY1eDwejI2N5QRstTO38hHnOq/Xi9YqlNwRAyXq/mQoFMrfxzQalSC+1+tFMpmcdxvRHitkWcaBAweQTqexdu3aWeeuZDKJSCSCSCQCWZZnpkRXUSqVUhIERJ9veHhYOW6sXr0657wrjtcWiwWdnZ0IBoOIxWIYGBhAe3t7xZlvBw8eVLLE1G0iHo8jHA4r62gEg0EkEomyM3qLEf01MZiwKET7EjNqClAnrExNTeUP2maJfpH47rxeL/x+P1paWuByuSo+vsuyjPFspr3P5ysctFW9tgjCivOYGLhTB2eTySSSqgSM+RofH0cqlSq6fZWYy3b5fD6l79/U1FS1a1RJknIGQMXzqq+/k8kk0uk0hoaGkEwmMTY2Bn02E9JoNBbsi8qyDL/fr3x+ItDf3Nw8a18QP4s+rtPpzAm4qde3SSaTSr+i2vR6PfR6PVKpFA4cOIAmTTmTUCgEq9Wac46cmprC+Ph4bvZphbTnUADKuTudTivnA+01iRgMFQP3fX19ADKfl8FggMlkgjE7mFnJdojvR/sZi/1QWyJO/V1OTEwgEAigt7d3Xu1UfA+FjpnaQLG4LonFYspxpbe3d1GulZYSBm2pfOoDQL4pbtmaNX6/P2+mrVo8HkcwGFROOuKfyCAqdpt47nwHsmQyWZWOWrnEQVKM+I+OjipB29HRUeh0uqpf4GkvOAWRoTgxMaFkpag7VjGzGQmRhTLzBjKLG1QwTQkAEl4vEmNjsJhMSEWjSCQSCIfDyvb4fL6cEeVimcCikzaXg7M48FutVsTjccRisZxgm3iMOmgL5AYrKs02k2UZkUgEfr8fwWBQ+T7E+4vH45iamso5KYl2MqVa9TUej8NsNlc9YFpoJFlNfcHj9/thNBqVoG4sFoPX64Uhm6nqcDgq3kb1BWcsFkMymVQC1CLb2WQyYXp6GsFgUFkQTHwXzc3NCIVCiEQiyih/W1tbWSOv4vvIF7wQry2Itiemn6vfp3o/a2pqUi6IU6mUss3idcSosM1mg9lszvsdiO/e6/ViQ55yHuI1w+HwrOlj8yWey+/3zwoSBgIBWCwWJTBTaNvmSv05lnPRm0qlcPDgwbyd+nKn4xUjPgsxU0T9OYsALgDl/0oCt+JCR2TxA7OP00BmfxQXclqxWAwHDx6cFZhIpVLK42VZzkyRjMVygvDiIkwEcS0Wi9Km1Reb4hwpLpLFY9TZjevWrVMC55FIBJ2dnQsauLXb7bOeXzuFrlpEINxkMuVtU2LQz+v1oqmpSQlWCYlEompBS9E+1INJQKZ9iu+72OtNT08r7SQcDqOzs7PsDBX1+7LZbPCppiGKY7F2u8S2zff9JxIJ5ZgqjqWSJKGxsRE+ny/nvKxuB+FwGCaTKe+xGqheO5FlGQMDA7MC5qFQKGcAVC0Wiyn9MKfTWZULTvH9JBIJ5X1qj+FA5jsR7WDWlOgKXisSiRT9+4MHDyKRSMzKfExnp20XOofq9Xr09PRgZGQEwWAQw8PDSCQSBaf1FxrsmpiYQG9vb077EIEgtblcC6RSKcRiMUQiEUSjUUSjUeX44PF4lKxW0Z8VGaAWi2XWsSsSiUCv18/7eqRYkFAdDItEIojFYgUHXcX7sFgsysBIPB7HyMiIUoKk0sy2cs/Hoh2EQiHs3bsX6XQa4XAYNput6DlFp9PBYDAofVF14FkQ/VVxvSECkuJ1Rb+rqalpXt+FnE1WyTeYnE88Hsf4+DiamppyrkfC4bByrdLe3j6vc6okSdDr9UpgVv3agtg31N/V2NiYkqyxNs+sDdEuxKw0i8WCtra2nONZvv61OG/6/X6sy5YQVAcFW1tbMTY2pgxsaVWjf2c2m5Xj4NTUVE7gUQRSLRYLent7IUmScn08PDyMnp6eioKk2u0W34WaaH+RSARrVOWl1DMFBHEuVPfp1AP06v+LbafRaEQikciUHcgOdouf1dsLYFbfBsjsU8XOc+UQ7UN9bhaZ+iaTadY5O197Gh0dxapa1x2uMwzaUtkk1YFEznOwiMfjyvQbLe3F+vDwcN6LgUrkq6mz2EFbIH+nKh6PK51Iq9WKVCoFt9utnPhDoRAaGxvnlNUmTshms1l5/4UuVMLhcE4HPBqNVjVDxmg0KiUR1J1mn88Hj8ejjLwGAoGCmRWDg4NKZ9Pj8VQUsBLvzeFwwOfzKReC6u+k1GeUTqdLXoSLi5lAIIBgMFi0c6HOEBJ27do1K3NmbGwM4+PjaGlpQSKRmDU6PV/q91usrqt6VNNgMGBwcFDJdAiHw/D7/UoASPwzm81lj8Luz66a29fXB7PZjOnpaSXjVP0csiwrbdvhcKCpqUkZ6ReLurndbrS0tBTMJlN3TPIFmrUdFNGp93q9mJycxCGHHJIT1BPPIy4sZVlGNBpFJBJBOBzOG8TV6XSwWq1KwCVfW1ZP81TfNjg4iEQigZaWFjQ1NSGZTOZchIj3l+9fvvu0xMWDy+WCy+XCYLburrpzps1onG+GozjOm83mksEEWZYxNDSEeDwOo9GInp6enHNKNTr1gtg/Cl0US5KEQCCgZDCWEg6HMTY2pgQTTCYTWlpakEwmZ13oicBOqefLx+12w+PxKEHbaDSKWCyGRCKh/FNfJOr1emWfFXQ6ndKxVwda1J+v+vwcDAYRiUTmnImipd0nxGCv2+2Gz+fD5OQkrFZr2VnxlVIHbbWBce20zampqVnH5UgkUrXzqNvtxtTUlHIMEdly6gvvQCAAk8k0K5NOfd4VF4wDAwPweDxlTctUHyu1QVsRJMz3uVe6H4pBBhGkjUQieZ9D7IsGgyFvFrjf78fIyAgcDge6urqU+7QBvLnIly0lgitqkUik4MWselA2HA5XNUtIfeycnp6e1Rbi8bgSZBHtuJxztBgMEv9K7Wf5BpoE7WevHUDV6XTo7OzE+Pg4vF4vJiYmkEgk0NbWVnafT+wX2qCxehBVDEYVI87l6n/5puBLkoSmpqacz3tkZAQNDQ0YHx9XBrTUbUKdnV6t8lPa6cbpdDqnjyEGpLXXFCLALtqxwWCA3W5HX18fpqenMTExodQbdrlceTMCC21HuTMhlDI3qkxAUc7BaDRCr9crwVnxs16vz2m/YjDXYDDkHLNFf0EcuzwejxLAVX+f+fpc5QoGg5iYmMg5FhT7TmVZRn9/v9I31D6X3++HLMtwOp1Fs6MLEecokdQEzHzGInNUUA/mNDY2wuv15gTv1J+LLMvKOU+WZaVETkNDQ973297eDp/PB4PBkNPnUGfeqpNaGhoaZiW0qM11sEkQ+6pom/F4PO91XzSbaKRuD8lkEvv27YPBYEBjYyMaGhrKvsYR2y2uhws9Zs+ePWhpacHk5GTB0m3iuxIDymIwTPu82mCuui+jzjgW1xDqz1xc30nZMpf5+Hw+ZSB9LscvdZ9SJAaJMjNr1qyZ9X7yfU+xWGxBMrKXMgZtqSzJ3/wGhn37Zm7IsxPnu8gU09C1J3dxMeZ0OpUpetp/Ytqi9p8kSXA6nfB4PNin3iZU96JeSx3wUR/E1BeW4kClPiCL0T29Xo9YLKYE9YLBIJqbm5Up1uVSX3Cqp9oL6oOcNjAejUbhdDoxOTmJQCCQc+EzV0ajUZnuJIiOgnrUUGRTqiWTyZw6O0NDQzAajWhqaoLb7S7ZMVJnD4iTlPaiQpxQtZ+F0WiETqdTFuTRXoSLTr2YOq1uW3q9Xgku58t6KbS94m/VnVgRwPF6vWhraytdXqPIc6tpOwUio67QKHcoFFLep+gIiM9O1KMT36XoMIggrqj1Wmy7wuEwzGaz0rnTTtMXAVDx3JIkwe12w+FwYHx8HD6fDz6fD8FgsOCUPnUHMd8FhPaCNJVKKRe6osMqOvz5yixIkqRk1YrOYSwWUwK44j0Uu7AV712biT40NKR83tPT0zAajRgaGlLqrOYbEa+UeP9OpxM2m015Xu30NTG7QV0TeS4XF8BMQMViscx6HbGSrcjsENmlOp0OXV1dMBqN6Ovrw9TUFPx+/7wCd5Vk/Op0OrS3t2NoaEgZgCp0QSoyatRBA4/Ho1zozLdurN1uz2lPVqsVRqNxVkkBkSUmgrjiuKYOyqiJIN3k5GTeMi3a/TMYDFYtaKtuxy6XC36/H1NTU5AkSclmy1diqb+/H7Iso6enZ85T98QgHYCcY1Y6ncb+/ftnHRvF4BUwk8ESDoertqiJWEB1amoKPp8vb9BWXISLRVwEv9+vXJStXr1aOU5OTEwgFAqho6OjaHBZnSGUb8BQnd2W7+9ESSZtdq/IpBMDXJFIZNbFlyRJMJvNsFqtSoaW+hypJRZIA2YCdNFoVLmoVW9zNalroOf7HZiZ0aTeZ8Lh8KzpuZXQntu0F91ifxb9CZ/PN2uwulC/Usx0E7NZCmW1Fup/NTQ0QJZlJVAmEijU02/VtbzVfR5JktDa2gqTyYSxsTFlsL2zs7PgMVY70CoGp4DcmQyiTyL2C+17VmfQimQDLTFbQf1PHGvE+UnUyBTEe3W73TAajTnXQtFodEFqM0ajUciyDIPBAKvVqtQWVotk66+qif6MyGp3Op1KPWgxeyxfSSptlqu2P51IJAp+f9rvwu12w2w2l+zjq+n1evT29gIAXs0uSKYe6BcmJyfh8XhmnXvnclwIhUKYnJxU9nl1W1P34UXWMJA59o2MjBTsY6gHxsSsqkqpg4TiMxfT8NUBWwBK2Qkg0+/TXstEo1El4Dc6Oqp8Tna7HW1tbUXPH263G263W7l2UEun09i3b1/OOUZcu6vL1+V7X/Nht9tht9tzajgDmRlTer1eyTCPx+MFZ+uOq2oGNzQ0lJxpqO5PFFugTdSgLtSPF9+NJElYtWqVElQVfTnxvwhGFwrmNjQ0KIOuYiBHvU2RSAS7du1CZ2dnwW0JhULYvXu38pzimqDU/6J9i3aZTCYxPDyc8xkEAgElu1Yd58mn2ufzpY5BWyqLnK3npsge7LRTOHMfIsFiscwK2ooToNlsLrm4xKzt0HQm1bVJgfxBW/V07FLPrc5W0v5Tvwf1wV7deUulUhgdHc2bRezz+XIuXMRjgZlMF/Gv2IlSHbQV1AvCqQ9y6qyveDyudPRE4HhiYiJvh1KMVIqOeCKRUH5Wj9yKAJt4TZ1OB6fTqXQA1Z9DvuCFuN9kMsHlcsHr9SKRSGB0dBSTk5NobGwsuGq6erRQr9cr02K0I9uiTWgvtkQxfHHiE4GLaDSqdF7VnQgRqBUBL0mSkE6nlaBtuQGl5ubmglOspqamKurI5iP+Vvt5i/2wUJafKB1hNBrR3d0NvV4Pm80Gh8OhBIPUUwbF5yZO0vku9vJlPBc6CYvvzWAw5Lx/vV6P9vZ2uN1ujI6OIhaLYWRkBD6fD21tbTmZJaXq2WqnrGtrFk5PTys1x4pl7AriGCcyqsRFxPT0tNIu8nVG1GU60um08hmL7UokEkoNzXQ6nXPcEO290D8xFVWWZYxkF94SUznVn6kkSbDZbAgGgzkXEgcOHIDBYEBfX59ynDh48OCcF4kQ+7g660wEe8R7NxgMStsCMpnW4ns1ZxcmyHchXgnxtyLwpmW32xEOhyHLMsxmM5xOp3J+efXVV2cNqKRSKaUmrTgO5VvcRX2cLhV012bj6/V6dHV1Yffu3crthc5jYn9VB1bT6bRy3B8fH89pR83NzQgEAohEIhgcHER7e3vO5yvOa6JNBoNBtLa2IhqNIhQKVbR6tPb9G41GZd9rbW1FMBhEPB7P6dxrieAfkJk219bWNqfArfjuRUaXEA6Hi74fkTl98ODBec8S0nK5XJiamlJmxvT39xcs2+BwOJQZO+JY3tTUpBwnbTYbRkdHEYlEcODAAbS3txcMDKiPl/n6HKK0jdhGMQAgjv3i9fv7+7Fu3TpMTU0hFAopdZHVx3ExA0H8UwfDxKItQr5jrrjQE0Q2o3afEtNCK2kbsiwrQdBCpYTEsUAcs8WArcPhwMTEhLK/OBwOBINBhMNhTE5OKlmDhTII821LPtr2INqx3W7Pe2xUB23FTCERqNWeg0XWo91uh9lsVhZLdTgceQelXS4XDAYDfD5fTiBGbMOBAwdyzjf5BspEMGR4eFiZKiwG6koRAWqdTqecwwDk1KtUfx7a7ENBDFaoA7TFZpX09PQgGo1iZGQkp72Kc1cwGERfX19OX1PU0ayWVCqF/fv3K6+vvl7QntfU53VB+/kaDAa0t7ejoaEBY2NjSkkqn8+H1tZWZRE5EUwSg6/a/vTQ0BCcTicaGhpyFmESgRm1atUmL9SXFLNCtANfgUAAdru9ZF3ZSCSCiYkJZd/X6XRoaGhAU1OTEjBWGxgYQG9vL6xWq5IQoz2Xd3V1YWhoKOe2fANA5RDvSySeALkJCGrqwRIxY059jSSOVeoBmNbW1oqmxtvtdiUgrH5ebaILgKKzSxOJRNXKxYmAtnj/YpDVbDYjHo8XDBA3Nzcr6weIrGO3242mpqaCxybxXAaDAW63W+nf56OeTdLU1JRzPaZOQhLfqxig1z6HOpgrArrJZBIejwc2m035HMW25etP+Hw+5bnVsRz1Nb14vUoTJort4+KcIvrc+bZNbAODtrkYtKWy6M48E3j+eeV3o9UKe3bF0Hg8PivjFcjNdhPT7AKBgBKwmktHRhvMMplMJYO2Pp8Po6OjyrQZMeKuDcqWcwFqMpnQ1NSkdFDV03KA3OmKWupMp+7ubkSjUSVDT0whEB1/k8mUs9iR+kSWL2irpg5mis/G7XZjfHxceU1BXCyLgJ46MFtuVp/6hGKz2ZQprtpMrXg8jomJCaXDL0mS0smwWq3K6L7P51OCt+Pj45icnERDQwMaGxtzPgf1aLMIHqtPAOrpISL7SHwWsiwrtaYCgYAyZSgQCOSczHU6nRKozVdzUafToa+vD8lksqyVVDs6Oma1e7vdjubmZmVqvChHUCxwW6z2owiIaDProtGo8nd2ux0OhyNv8LirqwsGgyEn60mMXgvJZFK5UBG11Eqd1P1+/6zBDzXRkSzUrq1WK1atWqWUMhBBicbGRng8nlmBVtHpiUQiyhQw0TbMZrOSYaTuFIiMpcbGxpx2VC7RMW5tbVWOA+opUyJAOT09ndOxE/ukKJUhptmazWZlAQ11ULZcdrtdmaqnJvYj9QWvIAavtINxBw8ehMlkUgYzxHaop09pfwZm9lNxURyNRnNKiGhfH5jdBvJN36+U+nghqAf9zGazMgtEBJgbGxuVzE+xwIZOp1O+P9HeHA4HWlpa8rZdSZLQ2dmpBOTEfimOk2pr1qxBKBRSXlNknKunt1UyK0On0ymfeygUyvmsDQYDWlpaMDY2hlAohImJibxT0RoaGnKOx6Jtigyj6elpZeEyq9Va8tglPpPVq1crGRrNzc3KxYvJZEJHRwcOHDiQ8zfqC1IxqCbqXIt2l6/9aRU6fxbKjhdBh+bmZuWCMx6PF63TXg71+VXUNk8mMwvFFGrnog2JAIT4W/Vq4GKhQ1GC6uDBg2hoaEBLS8usY4d2kKu5uVkpTREOh3POpw6HQ5lRk+/8rs5+AzL7mbpdFFuw0Waz5fTj8h1z1aUHgEwQplAfJd808WJEbXUASqKB2BfUg05iQE2c+8bHx5V2K0kSOjo64HQ6sWfPHiSTybylkoCZGq/aYK5er88baBPvCZhdFszhcMyqgShmgCSTSSUoo006sNlsSj9AGxTo6elRZhrl68+KGq6iLqT47vIlOACZ7zNfkMThcKCnp0cJEvf396Orq6tkWQnR9kVtR8FqtSrfm+gDqTMl1QMGFotlTiVOLBaLMsiqla8OdCgUUjI/50P8vfY4JY5/wOwFhvKdWwsFxSwWC3p6epS65qJck8PhUI6VVqtVOYbkG0QQi28Kfr8/b2LOfANz4v0OaZOJsgKBwKxjCjATXDcajXkX6hN9E3XZCRGsFdtcaNBXLGYl/lYMKg4NDSnJD2JGhfr1xAyWcvt16hmGJpMp5xpbW75BW8de9AfU7UIdaBULjc0lqN7e3g673Y6xsTFlpoWa+PzUx2WRrBOLxZRrr7m0jULnga6uLgwMDOScH0VbLpS8IkoLiWOHOAb6fD44nU40NTXNOreoEwIcDkfeoO2qVaty+jQNDQ1wu93KdqizuEvFRiRJyhvMVRP3iW3L16dQ1y93uVzKdyYWRNPOei71v3owR8wQzNenFI+x2+0FB8DFDLx8s2BXMgZtqSz63l4Mn3Ya2h99FKHeXvQefrhyn/ogq+5QqotLJ5PJWVMPqzHVUkwLEvIdmMRJcnJyEpIkFexIAzO1/gr9EydWMb1DBL0cDkfOQlz5qE8sFosFdrtdqfsqgqliSqE4WIkOvMlkUgK4+aZ2al9HnPzUB2QRaFYH6kS2WL4FAUSwQGThiJpTOp1OeY5kMplzgrHb7UpnWLy22WxWLnYmJyfh9XqVDoX2JKXT6ZR6QoFAQJl2JuqOut1uNDY25kzdFwd07YnUarUqQWn11GX1ggTi/2QyqbSTUoFaLbHojwjmd3d3zyowD0CZ0q+dGtzR0QEgM3VpenpaCdjMhQhei0AqMLNPBgKBnFHVfCdC8V5KMRgMcDgcSgaXuEgUnZJCdRDFvirqVao/i1LtWry/pqYmOJ1OjI2NIRgMYmpqCoFAAK2trTmBVtEWReBi1apVOZ1dcWEutqGlpUWptdfQ0FAya7cYSZKwdu1aZcE68V00NDQoo8vaEWSdTqdcSIhsADHdcq4KXQiI47LD4SjYedVunwj6iW2v5CJUXFx4PB5lVoDD4YDFYsl7PC4UtBXZJPkWfilFe7yQJAktLS3KYIuY2haPx5WsDPG/mNImahiq64q3traWPJc5nU44nc6c6XptbW1wOp0IBoM5dUnV712dKSfMNUvJbrfnLPgEZNqj0WjE4OAggsFgwWOCCOKJY6TNZlMWzRP7tdi3xYWhOkCi3WZt+RKRnRUKhWZlzwvqC1JxMVAsMzdfEFfMjgBmZ0BrgyHqc7T6MxODgaIO+1yCMeopq4LIaswXaFEbHBxU9j+RAZRvMLu3txcTExOYmprC9PQ0IpEIOjo6cj5b7WyCpqYmNDY2IpFIYP/+/QgGg8oxxGAwFK2TLh7T3NysLDBV7mfjcDhypszmO+Zq+1bicxKLVoqBJDEoX0nQVh30lGUZa9asyUkwENtktVqVgRdtVpvH41Gy07q6uhAIBJRkBfF9i8FwMXui2HRgdZYYMNO3VWdyiTrHor8lSRIcDgcCgcCs47qYKSQGYEsFiURWZb7bxfcq+m3qGpn5gs7FXstisWDVqlXK2gYDAwN5B7fV1AMv6rZisViUzzQWi2F4eFjZNnc2yaQaim3bgQMHcs6d6nqx+QLkpaj7NPmub2w2W05ZCpFVLUo1iXOKaK+lZg653W6lhJrX6805HmmzV4HZWXlq6tmEanOZpaGm/VuPxwOPx4NIJIKBgQH4/X40NTUVnAUaDodzgraibJ36/Fgou1J9rGpvb1euoUQpCnGestvtMBgM6OnpUY5FYp0GWZah1+sRj8eVxVAtFosykFKsfyO+Y/WMKyAza1Ick9va2mCz2ZREEGBmppPoq4hrKDGzqNyFfgsR/Ve/36+cy9XEdqrPu5IkwePxwO/3IxAIIBaLVbx/qKfWaz8zq9WKdevW5Rx/SpV7EM8jrm9EvycUCimJVWLNDfF5qfuWRqMRzc3Nykwy8dloz0cinrBq1Srlmn10dBTpdDpvuapKifcpSvxpB1mBmZlYQKa9rlmzJu/AdyV9TvV5MV+WvZrNZss7KGgwGHIGyBm0ncGgLZXNecYZ2HXooWhobIR6sp36gKg9GYudPRgMKgdWMTJcjQOTCK6IDLlAIICJiQk0NjYqU/nUHV8RIBCZH9qg7FwCNHq9Hq2trRgYGCh4gFKPeqpPtACUKV7qmkiiILk2iKu+uNceSLu7u5VFm6amppQAoQi2is6+evEuUUO0sbExJzArppYU6jiIjpi2bpsIcjY1NSmPEStFitfN9xlpLw4kSYLL5YLT6VSmW0YiEWXaudPpVLZNHNC1HQ6RRShG8oxGo5Ldpd5el8ulnBhEoHYu025bWlqUjM981MEicUGmnoLU1NSkdLTzKTSarL7d6XTOOsGJLDZ1/SP1NEK1udTTBQpf4BUiOoz5Ou7lnKCNRiO6urqUKcKJRAIHDx5U3pMYXOjq6lIuosbGxpTvRuw/6k6VWKRBLORUTnmEYgwGgxKUE8QxT1wUqakHCKpVLzMfMQIOZD5Hq9Wad1qd2G6z2YyOjg4Eg0GkUqmcWqrqDnOxn0WmgwgaxONxtLW1FbzY03ZwRYkHkTWs0+mULDG73V5Wp1KdMRiLxZSZDOJ5xewGdVaGuHhLpVLKlFFgJjiVr65yMeogjTgHam9TX9SIfWG+tYyBwoOkYgCnUBa80WjMWZEZgFLrzuVywWw2Kxn30WhUCeaqHy+mZRa6UBfZyIVWEVZrbW1VFgES06TzfT7F6qQBueeLfO3f5XLltAVBBEAOHDgAq9WKnp6eom1AzEpQl5jJ9zkUmoouiAEosc+0tLTk3T5BDErYbDZlOnd/fz9aW1uVi1PtbAJxwSYC9erzkTZoK5jNZrS0tCASicDlcsFoNBbMFi3EZrOhvb1dOdaq24DIuEmlUsqAqrp0h5hpIcsyvF4vxsfHMTExAafTWfbFpvpxojan9hgkbhMBBjWRkSeoS+ZoicV5RL9V+7Msy7BarTl1ckWNQvFZiVkrzc3Nyrk3kUgoCw6K7RP9vlKBoELE8c/n8ynlU/Kdm9QzIfJlrJf6HgwGA3p7ezE0NIRQKISDBw8qgyXFaBdENZlMyj6vLtklBsiqpVhfR/Q3rVYrGhoalHJp0WgUY2NjSqau0+ksa9aEesBXGwA1Go05Gewi8KPe/8T3VU7QVtDpdGhpaVEyAcV3Ks7daurburu7lTImjY2N6O/vz3uOn2/QVp0UIgJsIntcBJHFAKn62kvMrBHnJjEFXh3Qcrlc8Hg8Bb+b5uZmJJNJOBwO5bM1mUw5i2urryXV5129Xo++vj5luvn09LQyCC4C7ZOTkznXhDabLedYpE5uEDNVgNw+gpjRpf6cRN/TarWit7dXWYtEBAnnmwkuFJoVpV6MUNDOHopGo8ogo3rQ12w2FzyGiLrmer0+77WD9npM+73qdDr09PQgHo/nPUaI70Bsm1gwNBgMKv1FcY4Ury9KFIgZbmJmqfr7ENshjiVWqxV2ux2pVKoqi6mLbdH2xfLRDn7NlXa7C82wAGZmF6nbXUNDA0wmU06/OB6PVyVWtFwwaEtlczgcWHvIIUU7YEajER6PB0NDQ2hvb1ceK04o6szCahAXJpFIROmoiDqD2gtQccB0Op3o6Oio2tRGsdiHOrNVy+12KxdkpQ7IIiAhDlQiu0wcfEWtMTVxwJ+enlbqjKprsQFQVu4Ugdz29nbodDr4fL6K66i2trZibGwMzc3NsFgsMBgMOdPNRGdPlmUlSKI9cXg8HsTjceh0uoKfiXrEMxKJKCdN9UWTetqNujOoDeLmWwFVp9NVtT2KDoKoa6em7lD09vYiHo/nfI9i0aX50l5QisC0OiCgvSDt6OhAKpXKO2WsEiKwUIxer4fb7S4YvKikw+JwOGCz2ZRpedpAq8FgQEdHBwYGBnIuZMR0VG2JjYaGBkxMTGB8fHxO5RHyERln6sGNfFOoFrJjon4P2vfT0NCQN2glbhMr1JrNZiW4OZ+6y93d3crP+QY4ROdWe5v6mJtOp5XsTknKLA4njhP5Ou7qVYyNRmPOlM2GhgbE4/GiF+Ki5mg6nUZTU1PJmniFiAtK9X7vcrlyFvlSr/wr9lNRJ3w+mTAmk0lZ/E697aImpXrapro+nViYRxCzUcTPIjsImKnprF6ASpTfKbXytjb7Vvudq9+HJGUWM2ptbVVuF0Fa7cCB9p94brPZnHOM1ul0OcfJQhcxorQKMFNnV3x36rIxIkhbbnBCTJEVgVFRXkD0WxwOR07t+nIXsLHb7Vi1ahVGRkaU0hsio7nYbALtcaFQ0FZcZKnbwFyozz3qdiCybkT9WLHQlNhGdZmWxsZGZWbD+Ph42ZmV6u9ItKl82f5iQFfdzxOrjZd7nhCDZqUGJ9WZxwcPHsw5t3V1dSGZTCqfucvlQiAQUBYIE8TsnvkQtbyLZZWrZ0LkG/gp51gpBlnHxsZmlQ8StAOMdrsdRqNRqVUqSdKsttzd3V31hcBKfddWqxWdnZ3KWhWBQECZOiyODxMTE0otYVE/XUu9uJtIwhBlekS9WGAmKCTLcs4aA01NTfB4PDl9v0qCMyaTCd3d3QiFQpBledY5Uhz/RVtVnwuA/P3gSrchn46OjpwZMdrAj8gyN5vNaGhoQCgUQjQaRVdXl7I41sDAQM4ifGJx61IZ+jqdblbJB4fDgdbWVmWWXLHnUH9nbW1tAKCUowqFQkopExEYFI/VzrQUxw9tW2xpaVE+38bGRuW8pg5Iiv2hWvVj1Qod14rNnhAzFcVMLlHWRd12xCKD6kCuJEnKdWW+2aKltq+hoQGtra1lJZ1YLBZ0dnYiHo/D6/XC5/Pl1NnXli5Tv1/xHamDtvk+p3zJWHNV6LsVxwlxLNUOOM5HvqBtIaINar8Pse/kW4uDGLSlChU6EIgVrhsbG2G1WnHIIYdAkiSleL04MVZr9elS26Xd0c1mM/r6+ipeoKIQ9eix+mJbTYyaiQv9UheDhYhpbeIiLd97EAfLhoYG5USnnRZjMBjQ2dmJRCIBl8ulLFY0F42NjUqHWUwFVxM1C0OhkJK9mk6nYTAYMDExoUyVriTwY7Va0dXVpWQSBwIBZWqP+nPIV/tRPfV8MbS3tys1L8UFpvrkZDKZqjKamo/6dcRggrrjrs5OaW5uhizLVcvsbG1tRX9/P5xO56w6emazGd3d3Ur7F1PTRbZ8qZq2hYiamC6XS1mAR90Bs9ls8Hg8OReBIhAgAsxim0RZjlKL4lTCZDLNCh5YLBa0tLRAr9crC/vMZRXhcqkDTdr3Iy4GC9VgrGSacaXyHYvFMaUQ8XmKTn08HlcGtMbGxpQLYRHgAXIXn9K+pjrwV4hYmE0bWKxUV1eXMnVT0Ol0OYFsYCaQKzq2LS0tyuJo81Eoe00MfAhutzsnW0/dBkTGXr5zhwiGipXBgZlBR1GyRa/XF80QFQoNghZqj+ppfeUSpRssFgva29shSVLJ87T2+DQ2NgaDwZCzaJd2u8TAh7qW5r59+5BOp3NmYFitViWrbc2aNTnPo11IsJIpgyLQ5/V6MTExgUAgoGQoFXqvTqdTGbwSQaF8j6vWRaZ2e9XP39zcrGSNqgcFte1IkiS0tbWhv79fqUFYzmCY+N5Wr16tfL+i36UurSQG1NX7hrbvUy3qdqb+7sUsEvV+4HA4sH79ekiSlJPpWo1zSjkBDfHZqAcc5rISvPj+jEajsnCYeiBYPc0fmDkW9PT05GyLIGZPLIS+vj74/f5ZtZYB5GSNi6C+qJMfCoUQCASUBIxYLIbJyUml1IXD4VC2WV0GQmSVi4Ec7cCbyJwEMt97V1eXcr/6umsug62F9iGxza2trXkzucV5TGu+xwyRhZmPyEpXDy6LwQzRlkUSDJD5rDweT0UzxfJxuVxlBW3zETV2xXobIslFXVdcveaJ+Bsgtw/V1dWVs8+rrxsXS6Hzkvo77+jowMjIiNIvFslXzc3NcLvdSka0GOBIJBLKbFPxGYi+hjg2lhtbUJ9b5pJhbDKZ0NbWBo/HA6/Xi+npaaTT6aIBS3U9ZGCmpvlCEiXJJClTe3xkZASRSEQpEyfiEyI4Wg3q7167yLOW+L5EVrtY60YQfRZJkrgYmQqDtlQVnZ2dOavVioOAmCIsDrQLFbQVU9uBzAXuwMBATqdRbFc1ArZq6hIC6oPwmjVrYDQaC9bamY9870G8P1GXRl07Ud25qeYJvFRwzWg0KiN4er1eGVUWF7Bz/UzEdG2xqJy6TXk8HoTDYSUjxu12I5FI5GR9LwZRMkMdBFus11d/L6LzaDAYsHr1akxMTOQEr8uZglgJq9WKNWvWwGAwKJkM4nVElp9gt9uxevVqZaGuYDA4a0X3SphMJvT09OQd1PB4PAgEAsrJXwQARFaK+G70ej16enpy6m8v1PcmAneLMZigbhP53o/b7VbqumktZNAWmBnwE1nyhS7GOjo64Pf7lQtiq9WKlpYWpWa1KIWivhAWpRzUdTnnqlrTx7T7QT7aLNJyA51zpf2O7XY7urq6curQiUBtpe1BO+hYLpEh73K5cmq7VzM7yGg0Yu3atTnnIrHgVqGLeO3t6ixiEaAVmUDi/3zn7LVr1856nba2NgwODuY9JhTLli+HyLyz2WwYGhqalS2a7/GipJK4P9/7mG+wIx/t+xODvMDMSuXaBVoFMWXV6/VidHQUfX19Rft96ix87fOJ7C/1fc3NzUrQdiFnR9jtdqxduxbpdBr9/f3KtNlC70W0YbvdjpaWlpwVyBeDqAcOQKn9O1eibr2YbbBr1y7lNYRC+4CYMp5Op6vev1ET2YH5graF+sai3yFK7ogAbigUQjweV2YMieumfPW3Cx1/xSJXIgCm3daenp6qZ1aKbSl0bqrFtGYxCyPf7cBMUFccJ6oV1JckCV1dXcpCtvN5HjG419TUpNQoFpm46vIbWgt1fV2JQu1Tvb+K+s75jk/a2TtA7gwW8U8sGitU8t67urqU2StzZTBkFnIVC1prvw/1QlzqTFv1/wtJp9Nh9erVADKffXd3N2RZzhnwq/a1jbofVWrdC3WmrTqJQU0spMyg7QwGbakqik1x93g8CAaDs1Z6rTb1lJW+vj6lRt/k5GRZF8pzoT45qzOPROdIfdASxckXoiOprYOoLhOwEBdV81Gt2mL5Vs+02Wzo6+tTPv9qLTwxVwsd8MqnUNF9k8mUdyXfalN3TNRTh/JR13VatWrVnLLltApNuW9sbFRqwmkXfFLT6/VoaWlRFjlazGD/QlG3w0IXboUCNwt9IdDZ2Qm/36+sIlxon3G5XHkzwk0mk1KyQD2tMBwOI5FI5Ewz5oIG+eX77tUXNOoMpYXKXNNSLyLidruRTqcX5FymPd6oM9QKbVd7e7uSvR6LxZTgbKEAbT5iwTY1UXe9EDFjQAyAzoXFYkFfXx9GR0fh9/uLZo+LRcnEOVtkQorZOqKObbUVOweI+sfFNDc3IxgMKoufaoNYaurBOe13Z7Va0dTUlJOQYDKZsHr1ani93gUdSAFm9kuxqGW5s1AKXQQvJJfLpQz6iZIA86nFna9/Zzab0dnZibGxsaJtQNSoXMja8ED+vkaxtqam1+uVc1o6nVYW+gwGg0gmkznnrXL6kQ0NDUWnOVfrPG61WuH3+5Xs0GJ0Ot2869dWW6F+RDUsRGar6IPZbDY0NzcrNbBFmxAloZxO56IO0hRisVjQ3d2tJE+IhV6155hKtlW78DGQmT0lzr+itnO5qvkd6fX6gvudWDdDW994sYLr2nJT1UweK0Wd+Q9kjk8Wi0X5nhaiNMdKwE+NFpzo5C7mCUU9mrQQB8je3t5Zi3Cp673lOziKFU6rSYzkaS+krVYrAoGAMkViJalFoLQQh8Mx71VZ56KxsRHBYLAmF29CqexOrYUeXBB1SWVZVi4Gu7q6MDk5OWu/FCUDii3Gt5RoS4XkIzqf6XRamRlRSRBqrnQ6ndLpne9+IuoliyBfOBxWLoRTqVRdZKLUq0IlD4S2tjaEw+FFnW4p2t5iHz9LEcGKxR4Q9Xg8FdVPLUTUchfBi2LHBG3Nd/UF6mKca+cS+NPpdGhtbcXBgwfh9XrhdDoLflciI7TQRWS+IJyYIrtYFmPwbL48Ho+yfoEorVGteoSdnZ2IRqPKglOlBv6LLQRXTeq+gSRJSlm4Sul0OjidTjidTsiynBPATaVSiz7FvZiGhga4XK6y+wWrVq1SykEEg8Fl0Z+qJW3dU71eP6uUTq2p12MRqn0dmm9Qp96IEk/i3GK1WrF27dplfU0uysaIkpDhcFhZDJLmj0FbWhTLbVQlX00lESir5iq1paxZs0YJLqmJumtcdbG2qlnkvRLa6dW1oO5Q1UMnRafTKQu9iQuHYpkRi1kDeaGpj7/FLqRFx0oEbRc6U2khiZXmHQ6HslpzPbTDemW32/PWHxREJinVznxrKmvVc/+goaEBPp9vzgOPYnV3v9+PkZERZSaHVrHF2Kgy6mNEY2MjJiYmqhJsFgHNeleNgKR68V2xeGI9ZFCqVZol2djYCJfLhcnJyWXVr6Li9Ho9rFZrTu32lcTpdM4q+bXczzNms1kZ6BQL9tZzP2OpWd6th2gR2Ww2pZ7nYil0Aed2u2et+k20mOotaAtUv6b1UqJeNbaU3t5eRKPRmgw4LIRCiyjRDBGwXy7fOS1tbW1taGlpmdcxu6WlRVno0ev15g0AM2i7MJqammA2m+suS36hLEQG6WJPaV5IYp0HYG7Z87Q09fb2KovArTRiAcKVSpSlo+phL4WoiuppNLHep9PR8lZpeQRaWH19fWXXoCy2OjMtT2K1XqJ6Md9BNrFYzMjICCYmJuBwOGbVPmTQdmGIjFFgZQTpVvKAMFExKzFgS7QQeJYhIqKqE6vGF1rlmxaXyWSC2+1mB5qIVgy3262sQi0WolQTQdt6GnCnpaO9vT1v7WciIqJq4pU0EREtCGbuERFRLbW1tWH//v0Ih8Pw+Xw5dTVLLURGVIxY+JKIiGghMdOWiIiIiIiWHaPRCI/HAwAYHx9XsmsBlkcgIiKi+segLRERERERLUuNjY2wWCxIpVIYGxsDAKTTaaRSKQAM2hIREVH9YtCWiIiIiIiWJUmS0NbWBkmSEAgEEAwGlSxbnU7HxTKJiIiobjFoS0REREREy5bFYkFjYyMAYHR0FPF4HACzbImIiKi+MWgLYNeuXXj3u9+NrVu34tprr4Usy7XeJCIiIiIiqhKPxwOTyYRkMqmUSWDQloiIiOrZig/axuNxnH/++di4cSPuv/9+7NmzBw888ECtN4uIiIiIiKpEp9Ohra0NAJBIJAAwaEtERET1bcUHbR9//HEEg0Fceuml6O3txUUXXYT77ruv1ptFRERERERVZLPZ4Ha7ld+NRmMNt4aIiIiouBUftN2xYwe2bNkCq9UKANiwYQP27NlT460iIiIiIqJqa2lpUTJsmWlLRERE9WzF91SCwSC6u7uV3yVJgk6ng8/nyxmJL0WW5SVVC1ds71LaZqo+tgMqhG2DtNgmSGBboHyWSrvQ6XTo7OyE3++H0+ms++1dypZKm6DFwfZA+bBdkNZKaRPlvr8VH7TV6/UwmUw5t5nNZkSj0YqCtn6/Hzrd0klclmUZ4XAYQCZQTSsT2wEVwrZBWmwTJLAtUD5LrV2YzWYEAoFab8ayttTaBC0stgfKh+2CtFZKm0in02U9bsUHbd1uN3bv3p1zWygUqrjGlcvlgl6vr+amLSgR1Xe73ct6R6Di2A6oELYN0mKbIIFtgfJhuyAttglSY3ugfNguSGultIlUKlXW41Z80HbTpk05C48NDg4iHo9XlGULZEYAllqDEtu81LabqovtgAph2yAttgkS2BYoH7YL0mKbIDW2B8qH7YK0VkKbKPe9LZ35/Atk69atCAQC2LZtGwDg5ptvxkknnbSksmaJiIiIiIiIiIho+VjxmbYGgwFXXXUVLr74YnzjG99AKpXCHXfcUevNIiIiIiIiIiIiohVqxQdtAeD000/HH/7wB7z44os4+uij0dTUVOtNIiIiIiIiIiIiohWKQdustrY2tLW11XoziIiIiIiIiIiIaIVb8TVtiYiIiIiIiIiIiOoJg7ZEREREREREREREdYRBWyIiIiIiIiIiIqI6wqAtERERERERERERUR1h0JaIiIiIiIiIiIiojjBoS0RERERERERERFRHDLXegKVOlmUAQCqVqvGWVEaWZaTTaaRSKUiSVOvNoRphO6BC2DZIi22CBLYFyoftgrTYJkiN7YHyYbsgrZXSJkQMUcQUC2HQdp7S6TQA4MUXX6zxlhAREREREREREdFSIGKKhUhyqbAuFZVOp5FMJqHT6Zb1KAARERERERERERHNj8goNhgM0OkKV65l0JaIiIiIiIiIiIiojnAhMiIiIiIiIiIiIqI6wqAtERERERERERERUR1h0JaIiIiIiIiIiIiojjBoS0RERERERERERFRHGLQlIiIiIiIiIiIiqiMM2hIRERERERERERHVEQZtiYiIqCiv14tnn30WU1NTtd4UqiG2AyqEbYOIiIio+hi0rWOPPPIITjvtNBx++OF4z3vegz179gAAdu3ahXe/+93YunUrrr32WsiyXPJvSt1XTLHXAzId9Te84Q0YHByswrumfJZCW7jqqquwYcMG5d8ZZ5xRpXdPhSyFdnH//ffjbW97G4499lhcdNFFvKBfBNVuFw899BDOPPNMfPWrX8Wpp56Khx56qKzt4LmjtpZCO+B5ozaWQtvguWNxVbtNqH3iE5/AAw88UNZ28LxRH5ZCe+D5Y/EthXbBc8fiqnabmOt+vezPHTLVpQMHDshbt26VH3roIXl8fFz+7Gc/K7/vfe+TY7GYfOqpp8pf+cpX5AMHDsjnnnuufN999xX9m1L3FVPs9WRZlicnJ+X3vve98vr16+WBgYGF+TBWuKXSFt73vvfJf/7zn2Wfzyf7fD45EAgszAdCsiwvjXbxt7/9TT7yyCPlv/71r/Lg4KB87rnnyu9///sX7kOhqrcLn88nH3/88fLOnTtlWZblX/7yl/Ipp5xScjt47qitpdIOeN5YfEuhbfDcsbiq3SbUfvWrX8nr16+X77///pLbwfNGfVgq7YHnj8W1FNoFzx2LayHaxFz265Vw7mDQtk796U9/ku+66y7l9yeeeELeuHGj/PDDD8tbt26Vw+GwLMuy/Morr8jnnHNO0b8pdV8xxV5PlmX5ox/9qPzTn/50Se8E9W4ptIVEIiEfddRRcjAYnOe7pXIthXbxhS98Qf6f//kf5bG7d++W169fL09NTc31bVMJ1W4XQ0ND8q9+9SvlvldeeUU+6qijSm4Hzx21tRTaAc8btbEU2gbPHYur2m1C8Hq98kknnSS/8Y1vLCsYw/NGfVgK7YHnj8W3FNoFzx2Lq9ptYq779Uo4dxhqnelL+Z166qk5v+/btw+rVq3Cjh07sGXLFlitVgDAhg0blJTyQn9T6r5iir0ekElh7+npwde//vUK3yGVaym0hZ07d0KWZbzzne/E6Ogotm7diquuugqdnZ1zeMdUjqXQLrxeLw4//HDlsTpdpiKPwcBTz0Kpdrvo6OjA29/+dgBAIpHAj3/8Y5x55pklt4PnjtpaCu2A543aWAptg+eOxVXtNiFce+21OP300xGLxcraDp436sNSaA88fyy+pdAueO5YXNVuE3Pdr1fCuYMteAmIx+P48Y9/jI997GMYGBhAd3e3cp8kSdDpdPD5fHC73Xn/ptjzAcAFF1yAf/7zn7Me97nPfQ7BYLDo6/X09FTvjVJJ9doW9uzZg0MOOQSXX345Ghsb8T//8z+44oorcOutt1bvzVNB9douDjvsMPzxj3/ERz/6UUiShAceeACbN2+G0+ms3pungqrZLnbs2IGPfOQjMBqN+O1vfwuA546lol7bAc8btVevbYPnjtqpVpt48skn8cQTT+DXv/41vva1rym387yxtNRre+D5o7bqtV3w3FE71WgTxfbrlX7uYNB2Cbj++uths9nw3ve+F9dffz1MJlPO/WazGdFoNGcnUP9NsecDgK9+9auIRqOzHtfQ0IAf/OAHZb0eLY56bQtvf/vblUwbAPjKV76C008/HcFgEA6HY17vmUqr13bx8Y9/HOeffz7e9a53wWw247nnnsO1115bjbdMZahmu9iwYQN++tOf4tprr8Wll16KG2+8keeOJaJe2wHPG7VXr22D547aqUabiMVi+K//+i9ceeWVs/ZlnjeWlnptDzx/1Fa9tgueO2qnGm2i2H690s8dDNrWub/97W+4++678Ytf/AJGoxFutxu7d+/OeUwoFILRaCz4N8WeDwCam5sLvn45r0eLYym1BZfLhXQ6jbGxMXaeFlg9t4uGhgbcfffdOHDgAH70ox/B5/PhrLPOmu9bpjJUu11IkoTDDz8c11xzDU499VT4fD6eO5aApdQOeN5YXPXcNnjuqI1qtYnvf//7OOKII3DKKafMeg2eN5aOpdQeeP5YPPXcLnjuqI1q9ycE9X69Zs2agq+/Is4dtS2pS8X09/fLJ5xwQs4CD3//+9/lM844Q/l9YGBA3rRpk5xMJgv+TbHnK6XU6wlLubDzUlDvbeHrX/+6/NBDDyn3/e1vf5MPPfRQpSA4LYx6bxdCOByWTzjhBPnhhx+u6P3R3FSzXfz973+Xr7nmGuX30dFRecOGDbLf7y+6DTx31F69twOeN2qn3tuGwHPH4qlmmzj11FPlI488Uj7mmGPkY445Rt64caO8efNm+b/+67+KbgPPG/Wj3tsDzx+1Ue/tQuC5Y/FUs03Mdb9eCecOXa2DxpRfNBrFeeedh9NPPx2nnXYaQqEQQqEQjj32WAQCAWzbtg0AcPPNN+Okk06CXq8v+DeyLBe9r5itW7cWfD1aHEuhLRx22GG4/vrr8dRTT+GJJ57AVVddhbPPPlspCE7VtxTahXD77bdjzZo1OP300xfq46CsareLNWvW4J577sE999yD4eFhXHfddXjNa15Tsj4Yzx21tRTaAc8btbEU2obAc8fiqHabuOuuu/Dggw9i27Zt2LZtG97whjfgs5/9LD772c8W3Q6eN+rDUmgPPH8svqXQLgSeOxZHtdvEXPfrlXDukORSV+RUE4888gj+4z/+Y9btf/zjH7Fjxw5cfPHFsNvtSKVSuOOOO3DIIYeU/JtC96kLNxfalnyvp7Zhw4aynosqt1TawnXXXYe7774bdrsdp59+Oi666CLYbLY5vmsqZam0C7/fjzPOOAO33HILNm/ePMd3S+Wqdrvo7u7GX/7yF1x99dUYGRnBa1/7Wlx55ZVoamoqa1t47qiNpdIOeN5YfEulbfDcsXgWok2oXXLJJTjuuOPwrne9q6xt4XmjtpZKe+D5Y3EtlXbBc8fiWYg2Mdf9ermfOxi0XaJGR0fx4osv4uijjy6rY7zUXo/Kx7ZA+bBdUD5sFwSwHVBhbBukxTZBamwPlA/bBWmxTVQPg7ZEREREREREREREdYQ1bYmIiIiIiIiIiIjqCIO2RERERERERERERHWEQVsiIiIiIiIiIiKiOsKgLREREREREREREVEdYdCWiIiIiIiIiIiIqI4waEtERERERERERERURxi0JSIiIiIiIiIiIqojDNoSERERERERERER1REGbYmIiIiIiIiIiIjqCIO2RERERERERERERHWEQVsiIiIiIiIiIiKiOsKgLREREREREREREVEdYdCWiIiIiIiIiIiIqI4waEtERERERERERERURxi0JSIiIiIqQZblnN/T6TTC4XCNtoaIiIiIljsGbYmIiIhoyfjjH/+Iz3zmM2U/fnR0FLFYrOD9v/nNbzA6OlryeT7wgQ/gpz/9qfL7ZZddhq985Stlb0chyWSy7MfeeeedeOSRR+b8WhMTE7j33nuRSCRybp+ensZvfvObOQeho9EoUqnUnLeLiIiIiGZj0JaIiIiIFsw999yDTZs2lfx31VVXlfV8brcbf/jDH/D0008rt6XTaUQiEcTj8VmP/8pXvoL3vOc9SlAxmUziueeeU+6/9NJL8eqrrwIAdu3ahcnJyVnPsWfPHjz77LPo6+tTbvvoRz8Ku92eEwBNJBIFA59PP/00Pv7xjyMSieTcfscdd+Ccc85BIBAo+d5/97vf4Wc/+5nyu8/nw5lnnomzzjoL73jHO5R/Z511Fj7/+c/P+vuhoSFcfvnlswKs27Ztw1e+8pWygrbvec97cN111+Xc9qc//QlHHXXUrGAwEREREc2dodYbQERERETLlyRJaGxsxOOPP17wMR/72MdgsVhybhsbG8PJJ59c8G8++MEPzrrtyiuvxPvf/37l95dffhmPPfYYrr/+esiyjGg0ir/97W+48MIL8ctf/hKHHHIIzGYzDIZMl/iKK66AzWbDj3/845zn/clPfgKz2YwvfelLym3hcBiyLOP3v/+9clsikYDdbsdf/vKXWdvW09ODnTt34pJLLsH//u//Krf/8Y9/REdHB5xOZ973+b73vQ/bt28HAKRSKaTTaWzatAkA8MlPfhJf+cpXYLVa8c9//hM/+clPcMsttyCRSMBsNivP4fP5kEqllKBqNBpFMBiEx+NBIpHAbbfdhpaWFvzwhz9U/ub1r389Xvva1wIApqamEAqFYDAYIEkSjEYjDhw4gI6ODkxNTSEWi8FqtWLPnj1IJpNIJpPYtGkT9Hp93vdERERERKUxaEtEREREC0YEREsxmUx5/+7ee+/Fhg0bAGRKErz//e9Xgpbbt2/H//7v/+Lb3/429Hp9TqAynU7j6quvBgB87nOfAwAcffTR+PnPf47jjz8e//Vf/4U777wTkiQBAB599FH861//wv/93//lbMeuXbvwy1/+Eh/4wAfw5S9/Wbn9kksuQTgcxne/+13ltng8Put9CG1tbbj++utx7bXXIhwOw2azYXh4GM8888ys11STZRmXXXYZDj30UEiShCOPPFJ5fb1erwS2//GPf2Dr1q3K/Wpf+tKX8Ne//lX5TE899VTEYjE89dRTuPPOOxGJRHDRRRdBkiT87W9/w2OPPYb/+I//UP7+9ttvx49//GPo9XpEo1Hs2LEDt9xyCx588EF8+MMfhtFoRCgUwpVXXolgMIi9e/fixRdfLPieiIiIiKg0Bm2JiIiIaMGUm22p0+ny/m4wGCDLMkwmEx599FG85S1vUYKzyWQSTzzxhJKlql4s7IYbbsD27dvxy1/+Es3NzUilUspzXnzxxXj55ZdzXi+RSOCCCy7AunXrcm6/6qqrIEkSbr/9dtx5553K7el0GgBw+OGHA4BScuCJJ55AU1NTznP88Ic/xHe+8x0YjUYAwPHHH5/z+Zx99tlIpVI4/fTTc4LAAHDmmWdi1apVuOSSS7B69Wp873vfg8FgwKWXXgq9Xo+f/vSnCAQCeOSRR+ByuXDDDTcAAD7xiU/AZrMBAG666SYAmQD0WWedpZSH2LFjB773ve/hv//7v/G2t70NoVAI3/zmN3HppZeioaFB2YYLL7wQF154IQDgwx/+ME4//XRs3boVfX19+NWvfoVHH30Ut99+O+6++2784he/wI033sgsWyIiIqJ5YtCWiIiIiBbU6OioEtzMJ51O45hjjsl7344dO3D22WfDYDAgmUziwgsvVLJjRZD2qKOOQjKZxEUXXYRPfepTSCQSeOWVV3DxxRejt7cXQCYLVgQxw+EwnE4nfv/73yORSOCpp57CunXrsG7dOgwMDKCnpwdAph7v2NgYNm/ejI0bNxbNtA0GgxgeHobL5Zr1HkwmE7Zu3Yrbb7+94Gfw5S9/GdFodNbtn/zkJ3HppZdi//79mJycxGte8xoAmdqyn//853HnnXdi3bp1Ssatz+fD7bffjo9+9KPKZ5tKpZSAsSBKJWzZsgXPP/88tm/fjr1792JqagovvPACXnjhBXzhC1+AyWTC3//+d+zbtw87d+7Erl27sGfPHvzzn//Eddddh49+9KOYnp7Gxo0bAQCRSCTvZ0BERERElWHQloiIiIgWVFtbW8matloik3XTpk14+eWXYTAYcPTRR+O6667DqaeeCiCzuNcHP/hBvPTSS5BlWcl2NRqN+MEPfoB3v/vd+OpXv6o859VXX413vetduPXWW9Hf349169bhta99LXbt2oVdu3bh8ccfx5e//GX09PQgGAzim9/8Ji677DI88MADSCaTCIVCynMlk0mkUinlNkmS0NnZmTfDtNwSEdrHybKMr3/963jsscfw1a9+Faeeeir27OKxHboAANXNSURBVNmDiy++WAnKms1mvO1tb8OqVaswNDSEY489FrfffrtSpmH79u0455xzYDKZYDAYoNPpcOSRRyKVSuEDH/gAvv3tb2Pv3r05pSV27tyJK664Al/4whcAAH/9618RDodx4okn4uWXX8bZZ5+ND33oQwCAL37xizj33HPx7LPP4tvf/jb6+vrg9/sRiUSwb9++osF6IiIiIiqMQVsiIiIiWjAi+FqpWCwGALBYLEowM51OIxaLKYFS8Zh0Og2dTpcT9JQkCXfffTd++MMfYvfu3fjmN7+ZU3LhzW9+Mz7zmc/kvOYb3vAGJSPV4XDgmmuuwWmnnYb77rsPd911F+66665Z23n00Ufn/P6HP/wBq1atyrktkUggkUhgfHy84PvNl2Ubj8chyzJ+9KMf4bbbbsPVV18Ng8GAL37xi2hpacl57J///Gf88Y9/xI9+9CPl/QPAEUccgZdeekl53Jve9CZ85jOfwVvf+lblNu1zJZNJADNB5C9+8YuIxWKIxWL4/ve/DyCTrXzXXXfhxhtvxCWXXIK3v/3tMBgMmJycxMjICH7/+9/jS1/6Eh588EGsX7++4PsmIiIiovwYtCUiIiKiBSOCkSLAmk++wK74O/VU+2g0qtRWVYvH47BYLDm3BQIBTE9PIxgMIhqNYmxsDPF4HGvXroVOp8Nvf/tb7N69O+dvpqamlGAnAJx++unK9n3kIx8pWh4hlUohEolAkiSkUqmcjNtEIoHnnnsOr33tawt+BgDwxje+Med3s9mMyy+/HLIsY8OGDfjd736Hzs5OXH311ZiamsLHP/7xnNdobm7O+7wDAwN47LHHcPbZZ8+6b3h4GLfddhtMJhP0ej0kScLQ0BCA3DrDTz31lFInd9OmTUom7XXXXYc3vOENyuNcLhfe//7340tf+hJ6enoYsCUiIiKaIwZtiYiIiGjBTE1NYXR0FJs3by76uK1bt+b8HggEYDKZ4Ha7MTU1BZ1Oh6effjonqCqkUinIspxz3yOPPIKvfvWrSk3Xt7/97YjFYvjXv/4FILOA2Nvf/vac53n66afzblsqlcKdd96Ju+++e9Z9mzZtApApZSDqxN522205i42dd955OO+885TfDx48iO3bt+OMM87Iea5IJJLzezqdxu9//3vcfPPNkGUZv/jFL3DIIYfgoYcewte+9jW0t7crj/X7/Tm/q+3btw/XXHMN3vve9wIArrjiClxxxRW455574HA4lNIJkiRBkqS8JR42bdqEn/70pzjqqKOUALnNZsMdd9yRE7QFgCuvvBLHH388Vq9enXd7iIiIiKg0Bm2JiIiIaMHs2rULp556Km666aaCj8lX03Z0dFSZtv/JT34S+/btQzgchtvtzgnO+nw+HHXUUbNKF5x99tk4++yzccMNN2D37t1KRqywatUqvO51r8u5TZutK7z1rW/Fpz71KZx00klF3yuQCd6qn+ehhx7Ctm3bcMstt+Css87CRz7yERx99NG47LLLIMsyzjzzTADA7373O3zzm9/Ebbfdhq6uLgBAKBTCd7/7XaUkhPpzOu+88/DOd74TP/zhDwFk6tC+853vVO4X9X2BzGfp8XiUOrdXXnklTj31VFitVuj1evznf/5nznt4+umnce+99+YEwk877TQEAoG873nDhg2zbvv1r3+NQw45pOTnRURERET5MWhLRERERAsiHo/jySefxCc+8YmK/3b//v1oa2sDADzwwAOQZRkf+chHsGXLFnz+858HAPzf//0frrzySlx99dV5M3DVEokE4vE47HY7AOAHP/gBbr755lnbm8/1118/Kws2n02bNuG+++7Lue1vf/sbPB4PgEytWL1ej7Vr1+Kyyy7DV7/6VZx88smwWq0444wz8OCDD+IjH/kI7rzzTrS3t8PpdOLXv/41vvOd7yAcDuOKK64AALzrXe+C1WoFkAk0j42N4ZVXXsH3v/99yLKM1atXK3VpgUxAd3x8HNu3bweQKXvgcDgAZMojJBIJ6PV66HQ6SJIEr9cLIBP4FXVtf/e738FgMCg1f19++WV87GMfw/XXX4/XvOY1yms9+eSTuOCCCwpm/RIRERFReRi0JSIiIqIFsW3bNvj9/pxFr/KRZXnWbf/4xz+wZcsW5XdJkvD1r38d//Zv/4aOjg6sWbMGV155Jb71rW+hr6+v6PP7/X5ccMEFOOKII5SauP/+7/8+ayGyU045Je/fO51O/Pd//zfe8Y53FHyNa6+9Vim9ICQSCfzpT3/C1772NQCAyWRS6sSeffbZOOmkk2C1WjE6OopnnnkG3/jGN/Dxj38cF154Ie655x4AyFuqAJhZJOz+++/Hv//7v+NTn/oUnnnmGXznO9/BrbfemlML+Nlnn8Upp5yCSy65BJOTkznPc8UVV+DJJ59Unk9sn8PhQCKRUG5X18vdtWsXLr/8cmzduhVnnHFGTsB8cnISbrcbTqez4GdFRERERKUxaEtEREREVTc6Oopvf/vbeNvb3qZM91fz+/347W9/i2g0ipdffhmnnnqqct/U1BT++c9/4kMf+lDO3/T09OAb3/gGzjvvPBgMBlx88cWz6qlq7d27F0888QTe9KY34SMf+QiA3NIBQCYDdmRkBIFAIGfxLcFgMCAejyMUChV8nUQiMetv//CHPyCRSODkk08GABiNRgwPDytlB0Qm8bZt23DjjTfiL3/5C773ve9heHg453nS6TTuvvtu3HvvvcprJZNJ+P1+XH755TCbzTjvvPPg9XrR1taGc845BzfddBOOOOII/Otf/8LLL7+Mq6++Gi0tLfjmN7+JH/zgB3jppZdw6KGH4txzz8XFF1+sZNCKjORQKIQXX3wRxx13nPKZPf/883jwwQdx3333YevWrbj++ushSRKCwSD8fj+i0Si2bduGjRs3Fv1OiIiIiKg0Bm2JiIiIqOp+9KMfwWq14tJLL817v8vlwk9+8hOMjIzg0EMPVWq7AsBdd90Fh8OBE044Af39/RgeHsYrr7yCJ598Ev/4xz/w+te/HgBw3XXXYdu2bTj88MPR2tqKpqYmnH766TlB4vb2dnzxi1/MKdEQi8VytuXZZ5/Fhz/8YaxatQpHHnnkrG1NJBK4/PLLcfnllxd9z8cee2zO769//evxve99D2azGQCwefNm3HDDDbjxxhtzslONRiM+//nPw+12A4BSy1e9veecc05OeYT9+/fjLW95C8444wxceuml0Ol08Hg8+OEPf4jPfe5z2LdvH4444ggMDQ3hxBNPVOrOXn311RgZGcHDDz+MRx99FHv37sXo6Cj8fj/S6XTO637xi1/Ecccdh+3bt+NDH/oQIpEITjjhBPzgBz9QAtFAJqB79tlnY3p6Gh6PB9/5zneKfk5EREREVJok55uPRkREREQ0T6FQSKkhm08sFlMCmmp79+7F7t27ccYZZ+D888/HwYMHccQRR+DYY4/FGWecgYaGBgDA9PQ0/v73v+P555/HK6+8Ap/Ph/vuu09ZcKsSo6OjSuar1pFHHllWeYSnnnpqVk3bhTQ+Pj4rwJtPqe8ByJSoSCQSSKVS0Ol00Ov1SmkEAHj88cdx6KGHorW1Ne/f79y5Ew6HA52dnSXrCxMRERFRaQzaEhEREREV4fV6Ybfb5xQMJiIiIiKaCwZtiYiIiIiIiIiIiOrI7JUWiIiIiIiIiIiIiKhmGLQlIiIiIiIiIiIiqiMM2hIRERERERERERHVEUPph1Ax6XQayWQSOp2OK+USERERERERERFRQbIsI51Ow2AwQKcrnE/LoO08JZNJvPjii7XeDCIiIiIiIiIiIloiNm3aBJPJVPB+Bm3nSUTEN23aBL1evyCvIcsy/H4/XC4Xs3kJANsElcY2QoWwbZAW2wSVwjZChbBtUD5sF1QK2wgVslLaRiqVwosvvlg0yxZg0HbeRCPS6/ULGrTV6XTQ6/XLutFS+dgmqBS2ESqEbYO02CaoFLYRKoRtg/Jhu6BS2EaokJXWNkq9Ry5ERkRERERERERERFRHGLQlIiIiIiIiIiIiqiMM2hIRERERERERERHVEQZtiYiIiIiIiIiIiOoIg7ZEREREREREREREdYRBWyIiIiIiIiIiIqI6wqAtERERERERERERUR0x1HoDiIiIiIiIiIhoeUvJMrYH/ZiKx2CKJ7DV5YJBkmq9WUR1i0FbIiIiIiIiIiJaME94J3HL4F5MJuLKbZ6JIZzbvQYnNnpquGVE9YvlEYiIiIiIiIiIaEE84Z3ENft25ARsAWAyEcc1+3bgCe9kjbaMqL4xaEtERERERERERFWXkmXcMri36GNuHdyLlCwv0hYRLR0M2hIRERERERERUdVtD/pnZdhqTSTi2B70L9IWES0dDNoSEREREREREVHVeUsEbCt9HNFKwqAtERERERERERFVXaPRVNXHEa0kNQnaPvDAA9iwYcOsfw888AB27dqFd7/73di6dSuuvfZayKq6Jot9HxERERERERERzc3hDhdsOn3RxzQbTTjc4VqkLSJaOmoStH3b296Gp556Svn32GOPobGxEUceeSTOP/98bNy4Effffz/27NmDBx54AAAQj8cX9T4iIiIiIiIiIpq7iXgM8XS66GM+2b0GeklapC0iWjpqErQ1mUxwuVzKv23btuHMM8/E3r17EQwGcemll6K3txcXXXQR7rvvPgDA448/vqj3ERERERERERHR3MiyjB8O7EUSmRnNFt3sENQZnjac2OhZ7E0jWhIMtd6AWCyG2267Db/4xS+wbds2bNmyBVarFQCwYcMG7NmzBwCwY8eORb2vUrIsL1hpBfHcLN1AAtsElcI2QoWwbZAW2wSVwjZChbBtUD5sFyT8fXoSz/i9AIAmownfPXQL9kXC2BH0486RAQDA074pRFNJmEuUUKCVYaUcP8p9fzUP2j744IPYsmULuru7EQwG0d3drdwnSRJ0Oh18Pt+i3+d2uyt6H36/H7o8o0bVIMsywuEwgMw2ErFNUClsI1QI2wZpsU1QKWwjVAjbBuXDdkEAEEmncPPATFLcBxrbkAqF0Qugx+rEdrMNz8XC8CYTuH9gP97a0Fy7jaW6sVKOH+kSJUOEmgdt7777bnzmM58BAOj1ephMuSsGms1mRKPRRb+v0qCty+WCXr8wI0MiAu92u5d1o6XysU1QKWwjVAjbBmmxTVApbCNUCNsG5cN2QQBw7+A+TKeSAIBjXY04rbNbaQ+yLOPf4h14/uAeyAB+65vEO7pXwa6veYiKamylHD9SqVRZj6vpHnHgwAH09/fjpJNOApD5Unbv3p3zmFAoBKPRuOj3VUqSpAVtUOL5l3OjpcqwTVApbCNUCNsGabFNUClsI1QI2wblw3axsr0aDuI348MAAJOkw6d61syamdxttuD1jS34s3ccgVQSD44N4/2dvbXYXKozK+H4Ue57q8lCZMJvf/tbnHLKKUqQdNOmTXjhhReU+wcHBxGPx+F2uxf9PiIiIiIiIiIiKl9KlvH9/lchJn+f09GDNrMl72PP6eiBHpng1baxg/AnE4u0lURLQ02Dtn/5y19w/PHHK79v3boVgUAA27ZtAwDcfPPNOOmkk6DX6xf9PiIiIiIiIiIiKt9vx4exJxwCAKyy2PCOts6Cj203W3BGcxsAIJpO4/6RwUXZRqKlQpJrtCRbNBrFsccei1/96ldYu3atcvsjjzyCiy++GHa7HalUCnfccQcOOeSQmtxXjlQqheeffx5HHnnkgta0FYujLef0cCof2wSVwjZChbBtkBbbBJXCNkKFsG1QPmwXK9dkPIb/2P4cIulMvc5r1m/CYQ7XrMep28hUIo7zX34WcTkNk6TDTRuPhsdkXuxNpzqxUo4f5cYSaxa0LWZ0dBQvvvgijj76aDQ1NdX0vlJWStA2JcvYHvTDm4ij0WjC4Q4X9Mt4B6p39dAmqL6xjVAhbBukxTZBpbCNUCFsG5QP28XKde3eHfj79CQA4AxPGz69al3ex2nbyI8H9+FXY0MAgDc3t+P83rV5/46Wv5Vy/Cg3lliXS/O1tbWhra2tLu4j4AnvJG4Z3IvJRFy5zWM04dzuNTix0VPDLSMiIiIiIiKiWnvaN6UEbN0GIz7atarsv313Wzd+PzGCaDqNP0yM4uy2roJ1cIlWkprWtKX694R3Etfs25ETsAWAyUQc1+zbgSe8kzXaMiIiIiIiIiKqtVg6hR8O7FV+/39dfXAajGX/vdtoxNtbM7VvU5Bx9/BA1beRaCli0JYKSskybhncW/Qxtw7uRar+KmwQERERERER0SK4Z3gAY/EYAGCz041Tmloqfo53tnXBoc9MBv/z1BgGIuGqbiPRUsSgLRW0PeiflWGrNZGIY3vQv0hbRERERERERET14kAkhG2jmXq0BknCeT1r5lSL1K434Oy2LgBAGsDPh/uruZlESxKDtlSQt0TAttLHEREREREREdHykJZl/KB/D1LIzL59d1s3ui22OT/f21o60JAtq/C36UnsDQersp1ESxWDtlRQo9FU1ccRERERERER0fLwyOQoXgkFAACdZgv+rb17Xs9n0evxHtVz3DHEbFta2Ri0pYIOd7jgKRGQbTaacLjDtUhbRERERERERES15ksk8LODB5Tfz+tZC5Nu/iGmNza3o8VkBgA84/fiFZZjpBWMQVsqSC9JOLd7TdHHfLJ7DfRzqFdDREREREREREvTTw7uQzCVBAC8vrEFR7oaqvK8Rp0O72vvUX6/Y+gAZC5+TisUg7ZU1ImNHlyy+lC4DIac25uNJlyy+lCc2Oip0ZYRERERERER0WL7V2Aaj06NAwDsej0+3t1X1ed/g6cVnWYLAOCloB8vBHxVfX6ipYJBWyrpxEYPrl2/Wfn9CLsLNx9xLAO2RERERERERCtIIp3GD/r3KL9/pLMPDVVe50YvSXh/R6/yO7NtaaVi0JbK0mIyQxRBiMtplkQgIiIiIiIiWmHuHx3EUCwKANhgd+LM5rYFeZ3XNjajz2oDAOwOB/FP39SCvA5RPWPQlspi1OnQYDQCAMYTsRpvDREREREREREtpqFoBPeNDALIBJMu6FkL3QIldOkkCR9QZdveOdSPNLNtaYVh0JbK1mLMrODoTSSQSKdrvDVEREREREREtBhkWcZNA3uQyAZO39HahT6bfUFf8zh3E9bbHACAA9Ew/uKdWNDXI6o3DNpS2VpMZuXnyUS8hltCRERERERERIvlce+EsiBYi8mMczp6Fvw1JUnChzpXKb//fLgfSZkJZLRyMGhLZVMHbcfjLJFAREREREREtNwFk0n8aHCf8vunutfAotcvymtvcTVgk8MNABiORfGnybFFeV2iesCgLZWtmUFbIiIiIiIiohXltqH98CUTAIAT3E04rqFpUV//Q50ztW3vGR5AnOUaaYVg0JbKps60nWDQloiIiIiIiGhZ2xH04/cTowAAi06Hc3vWLPo2HOpw4VhXIwBgIhHH7ydGFn0biGqBQVsqG8sjEBEREREREa0MSTmNHwzsUX7/YOeqnBm4i+mDqmzbe0cGEU2larIdRIuJQVsqW07QNsGgLREREREREdFy9eDYMPZHwgCA1VY73trSUbNtWWNz4DUNHgCAL5nAr8eHa7YtRIuFQVsqm1NvgFmXaTLMtCUiIiIiIiJansZiUfx8uB8AIAG4oHct9JJU0236QGevEsR6YHQQwWSypttDtNAYtKWySZKEFmMm23YiHoMsyzXeIiIiIiIiIiKqJlmWcfPgXsSyC369uaUd6+3OGm8V0G2x4ZSmVgBAKJXCtrGDNd4iooXFoC1VRNSviabTCKQ4qkVERERERES0nPzDN4WnfF4AQKPRiA91rqrxFs04p6MHhmzG74NjQ5hOxGu8RUQLh0Fbqoi6ru0ESyQQERERERERLRvhVBI3D+xVfv9k9xrY9YYablGuNrMFZza3Acgkk90/ymxbWr4YtKWK5CxGxqAtERERERER0bLx8+EBTGazV49yNSiLf9WT97T3wCRlwlm/HR9mQhktWwzaUkUYtCUiIiIiIiJafvaGg/j12BAAwCTpcH7PWkg1XnwsnyajCW9t6QAAJGQZvxgZqPEWES0MBm2pIs0mk/Izg7ZERERERERES19KlvH9/j1IZ39/b0c32s2Wmm5TMe9q74JVpwcAPDIxhuFYpMZbRFR9DNpSRZhpS0RERERERLS8/H5iBLvDQQBAj8WKd7Z21XiLinMZjHhnWycAIAUZdw8z25aWHwZtqSLNRtVCZFylkYiIiIiIiGhJm0rEcfvBA8rv/96zFkZd/YeL3t7aCWd2kbTHpsbRHwnXeIuIqqv+90KqK0adDo0GIwBm2hIREREREREtdT8a3IdwOgUAOM3Tio1Od423qDw2vQHvas9kBMsA7hw6UPwPiJYYBm2pYs3ZEgneRByJdLrEo4mIiIiIiIioHj3r9+Kv3gkAgFNvwMe6+mq7QRV6a0sHGo2ZxLInfVPYHQrUeIuIqodBW6qYqGsrIzONgoiIiIiIiIiWllg6hR/271V+/3/dfXBlZ9YuFWadHu9t71F+v3O4v4ZbQ1RdDNpSxbgYGREREREREdHSdu/IIEbiUQDAEQ4X3tDUWuMtmpszPG1ozcYpnvNP4+WAr8ZbRFQdDNpSxRi0JSIiIiIiIlq6BiJh/HL0IADAIEk4v2ctJEmq8VbNjVGnwzkdM9m2dwz1Q5blGm4RUXXUPGj7rW99C+eff77y+7Zt23DKKafgqKOOwsc+9jEMDg4q9+3atQvvfve7sXXrVlx77bU5O+FC3Ef5MWhLRERERLRypWQZLwZ8eHxqHK9EQkjxGopoSZFlGT8Y2INkdt89u60LPVZbjbdqfk5pakWX2QoA2B7y4zn/dG03iKgKahq03bVrF+666y5cdtllAID+/n5cf/31uPHGG/HQQw+hs7MTl156KQAgHo/j/PPPx8aNG3H//fdjz549eOCBBxbsPiqMQVsiIiIiopXpCe8kzn3paVy++yV8+8BuXDO8H596+Rk84Z2s9aYRUZn+ODWGl4N+AEC72YL3tHfXeIvmTy9J+GBnr/L7HcMHmJRHS17NgrayLOOKK67ARz/6UfT2Znas7du3Y8uWLdi4cSM6Ozvxrne9C/v27QMAPP744wgGg7j00kvR29uLiy66CPfdd9+C3UeF5QRtEwzaEhERERGtBE94J3HNvh2Y1CxGPJmI45p9Oxi4JVoC/MkEfjq4X/n9/J41MOv0tdugKjqxwYPVVjsAYE84hCenp2q8RUTzU7Og7S9+8Qvs2LED3d3dePTRR5FIJLBu3To8+eST2L59OwKBAO666y685jWvAQDs2LEDW7ZsgdWaSXffsGED9uzZs2D3UWFOvQEmKdN0mGlLRERERLT8pWQZtwzuLfqYWwf3slQCUZ376cH9CKSSAICTG5txlKuxxltUPTpJwoc02bY8JtFSZqjFi4ZCIVx//fVYtWoVRkZG8Ktf/Qo33XQTbrvtNrzxjW/E2WefDQDo7u7GvffeCwAIBoPo7p5J2ZckCTqdDj6fb0Huc7vdFb0nWZYXLPVePHc9pfa3mMw4GItgIh5DOp1esgXLl6p6bBNUX9hGqBC2DdJim6BS2EYIAF4O+GZl2GpNJOJ4OeDDJmdl11K0vPCYUb9eDvrwx8kxAIBNp8f/6+qryfe0kG3kaGcDNtid2BkKYDAawWNTYzi1qbXqr0MLY6UcP8p9fzUJ2j788MOIRCL42c9+hoaGBpx33nk466yzcMstt+DRRx/Fvffei7Vr1+Lmm2/Gueeei/vuuw96vR4mkynnecxmM6LR6ILcV2nQ1u/3Q6dbmMRlWZYRDocBoG6Cow06HQ4CiKbTGPJOwaGvSVNaseqxTVB9YRuhQtg2SIttgkphGyEAOBj0lfc4vw+96QXeGKprPGbUl7QsY2c0jKlkHPdPjSu3/1tjK/ThCHyILPo2LXQbOdvlwTWhAADgroMHsFlngoFtcUlYKcePdLq8E2VNIm0jIyPYvHkzGhoaMhthMGDDhg3w+/1461vfis2bNwMAPve5z+Huu+/Gjh074Ha7sXv37pznCYVCMBqNC3JfpVwuF/T6hakDIyLwbre7bhpth8+OlyMhAEDMYkGXzV7jLVpZ6rFNUH1hG6FC2DZIi22CSmEbIQDo0gEYGyz9OJcbbmbarmg8ZtSPJ6YncevgvllZ8h0mC97R0wd9jb6fhW4jJ7jd2BL04oWAD+PJBJ5ORvHG5vaqvw5V30o5fqRSqbIeV5OgbXt7O2Kx3FqoQ0NDePjhh/HmN79ZuS0UCiEcDiOVSmHTpk05i4QNDg4iHo/D7XYvyH2VkiRpQRuUeP56abTqxcgmEjGslRw13JqVqd7aBNUfthEqhG2DtNgmqBS2EdrodMNjNBUtkdBsNGGjc3lfaFN5eMyovSe8k7h238689w3Ho3jK58WJjZ5F3qoZC91GPtS5Ci/s/BcA4J6RQZzqaV02C64tdyvh+FHue6vJQmSnnHIK9uzZg5///OcYGRnBbbfdhldeeQUXXnghHn74Yfz0pz/Fgw8+iAsuuADNzc3YsGEDtm7dikAggG3btgEAbr75Zpx00knQ6/ULch8VlxO0jRevbUVEREREREubXpJwbveaoo/5ZPeammXuEdEMLhwIrLc7cZy7CQAwlYjjd+MjNd4iosrVJNO2oaEBt956K6655hpcc801aG5uxne+8x2cdtppiMfj+NnPfobx8XEccsghuOGGG5RyBVdddRUuvvhifOMb30AqlcIdd9yReRMGQ9Xvo+LUQdvxeKzII4mIiIiIaDk4sdGDtoNmjGr6/xadDp9btb6mWXtENGN70F/WwoHbg/5lvXDgBzt68ZRvCjKA+0YHcUZzG2xcj4eWkJq11iOPPBJ33333rNs//elP49Of/nTevzn99NPxhz/8AS+++CKOPvpoNDU1Leh9VFiLURW0TTBoS0RERES03I3FokrAttlowlQijjQAp96AExp4HUVUL7wlAraVPm6p6rPZcXJjMx73TsCfTOLXY8N4b0dPrTeLqGxLboihra0NbW1ti3Yf5edhpi0RERER0YrypG9K+fmNze14MeDDv4I+jCfiGIxG0GO11XDriEhoNJqq+ril7P0dvfirdwJpAL8cPYg3t7TDaah88XmiWqhJTVta+kw6HRqzB7oJBm2JiIiIiJa9J6YnlZ9PbPDgWHej8vvTfm8tNomI8jjc4YKnREC22WjC4Q7XIm1R7XRarHiDpxUAEE6n8MvRgzXeIqLyMWhLc9aczbadSsSRlNM13hoiIiIiIloo04k4Xgn6AQBdZit6rDYc45oJ2j7jY9CWqF5w4cBc53T0wpB9rw+ODeHv3gk8PjWOFwO+Zb0YGy19S648AtWPFpMZu8NByAAm43G0mS213iSimkjJMrYH/fAm4mjMjlivlA4QERERrQz/zC7mA2SybAGg02xBi8GI8WQC24N+hFNJLvJDVCeOcTfCrNMhls5NsGo2mvDJ7jUrauHAFpMZb2pux6/HhxGXZVy7b6dyn8dowrkr7POgpYNnVJqzFk1dWwZtaSV6wjuJWwb35qzOyhM/ERERLTfq0ghi0TFJknCkzYmH/VNIQcbz/mmc1Nhcq00kIpVn/F4lYHuUswFv8LSu6AST1QVqbk8m4rhm3w5cgkN5/UZ1h+URaM60QVuileYJ7ySu2bcjJ2ALzJz4n/BOFvjLhZOSZbwY8OHxqXG8Eglxug9RnVLvq5yaR0T1LphM4l8BH4BMlt46m0O5b7Pq52dY15aobjw+Na78/I62TryuqQWbnO4VGbBNyTLuGh4o+phbB/eyP0Z1h5m2NGfNDNrSCpaSZdwyuLfoY24d3IvjGpoWrWOUN+t3YohZv0R1hhn6RLTUPO2fQjIbzDixwQNJ1bc51GKHSdIhLqfxjM8LWZZz7ieixRdOJfGUbwoA4DYYsdnZUNsNqrHtQf+sRButiUQc24N+bHK6F2mriEpj0JbmTJ1pO5Fg0JZWlnJP/Be+8hxaTRbY9XrY9Iac/+16A2zZ/8XPNr0eVp2+4osdkfWrxek+RPWF+yoRLUVPTk8pP4t6toJJp8NmpxtP+73wJhPYGwlhrSr7logW35PTU0hkB1pe29i8IrNr1bwlrtsqfRzRYmHQluasxchMW1q5yj2hD0QjGIhGKnpuHQBbTkC3eMDXqtPjBwN7ij7nYmf9EtFs9ZihT0RUSiydwrPZsgdugxGHOlyzHnOMqxFPZx/zjM/LoC1RjT2mKo3w+qaWGm5JfWg0mqr6OKLFwqAtzZnLYFCmQjFoSyvNQp7Q0wCCqSSCqSSA6uxbnO5D9SIly9ge9MObiK+4xTA4NY+IlqLn/NPKYkbHufMPKh3jbgAGMz8/7ffivR09i7iFRKTmTcTxr8A0AKDNZMZ6DqLgcIcLHqOpaD/MqTfg8DyDUkS1xKAtzZkkSWg2mTAUi2I8HmP9KlpRDne40GAwYjqZKPiYZqMJ3994NGLpNEKpJMKpFEKpJEKpFMKa/3Pvz/wsfk9WqSA+p/tQra30Wq6cmkdES9ET0zMLq2pLIwitJgt6LTb0R8PYFQrAn0zAZTAu1iYSkcpfvRNIZ39+XVMLr9EB6CUJ53avyVuiSgilkvjH9CROamxexC0jKo5BW5qXFpMZQ7Eoouk0QqkUHAY2KVoZYukU9CjeAfpk9xqYdXqYdfo5X7jIsoy4nJ4V8FX/vj8cxJ+9EyWfi9N9qJZYy5VT84ho6Umk08piRjadHpuLzAI4xtWI/mgYMoBn/V6c0tS6SFtJRGqPq0ojvK6RpRGEExs9uASHzkogMEs6xOQ00gC+uW8nLpJlnMySElQnGGGjeVEvRjYejzFoSyuCLMv47oFXMZnMnOz1kJDCTDZss9GET1Ypc1CSJJilTOC3UCAnJct4scS06+bsNHSiWmAt14xypuZxXyWievJS0IdQKgUAONbdCKNOV/Cxx7gb8cuxgwAydW0ZtCVafMOxCHaFgwCA1VY7eq22Gm9RfTmx0YPjGppySnVtsDtx08Ae/HFyDGkA396/C2mwFjDVB0bYaF60QdvVNnsNt4Zocfxy9KAyVdCu1+Ob6zdjKpmoWY3Ocqb7fLJ7zbIOhlF9Yy3XDO6rRLTUqEsjnFCgNIJwmMMJm06PcDqFZ/3TSMkyj2dEi+wvUzOz717Haf556SVpVn/z073roIOEhydHkQZw/f5dSMky3uDh4BPVVuGhUqIyNKuDtgkuRkbL3wv+adw+dED5/T/71qPLasMmpxuva2rBJqe7JhcoJzZ6cMnqQ+HRZONadXpcsnr5Tzun+jZR5mKVK6GW64mNHqyxzh7gNEkS91UiqispWcY/pjOlEUySDke7Gos+3iDpcKSrAUBmQdVdocBCbyIRqciyjMdUpRE4xb98OknCBb1r8abmdgCZhaG/e2A3Hpkcre2G0YrHoC3NS6sqaFvuRTnRUjUej+Fb+3cqhf3f196Dre6mmm6T2omNHtxyxLG4ZPUG5bYmo5FBIKqpfwWmcYdqoKOYlVDLNZFOYygWAQA4dHpYdXoAgITM1GMionqxKxRQFlw9ytUAq15f8m+OUR3HnvZ5F2zbiGi2fZEQBrN9jMMdrpxZsVSaTpJwfs8avLWlAwAgA7jhwKv4w8RIbTeMVjQGbWleWoy55RGIlqt4Oo1r9+6AP5kEkFls45yOnhpv1Wx6ScKJjc04zO4EAByMRTEYDdd4q2glmk7E8Z39u/CV3S9joowM2pVSy3VXKIBoOjP0c2xDE05oyAz8xGQZLwf9tdw0IqIclZRGEI5RZeM+42fQlmgxPc7SCPMmSRLO7V6Ns1o7lNtu7N+D344P13CraCVj0JbmxaMavRtj0JaWsVsG9mJ3tqh/m8mM/+w7BLo6rtN2vCoDWExtJFoMKVnGb8eHccH2Z/Fn1RS9TrOl6N+tlFquzwemlZ+3OBtysvWf9nFfJaL6IMuyErTVQ8LWMmcCNBpNWJtd42JfJIRJXh8QLYq0LONxb6bfpYeE1zBoO2eSJOETXavxztZO5babBvbi12NDNdwqWqkYtKV5Mel0aDAYAbA8Ai1fD0+M4g/ZekYmSYdL1hwKZ7bd16vjVRkxT6oyZYgW0p5wEF/a+S/cNLBXWW3coTfggt61uPHwo/PWXQaA/+hdu2LKeLwQ8Ck/b3G6caSrAXpkgtVP+b2QZblWm0ZEpNgXCSkJGUc4XRX1e5htS7T4XlEt+nqUqwGuOr9WqXeSJOFjXX14d1uXctstg/vwq9GDNdwqWokYtKV5E7VyphJxJOV0iUcTLS27QwH8cGCP8vsFvWuxxuao4RaVp8NsQXd239wVDjLThRZUOJXELQN78fkdLygZ6QDwhqZW3Hj4UXhjczt0kqTUXf7aIUfgONdMhqmo67rchVJJ7M4uzNNtscJjMsOuNyhlIUZiURzM1qIjIqoldWmEE8ssjSAcq5pB8Azr2hItise8M7ObXscFyKpCkiR8uHMV3tverdz244P78UsGbmkRMWhL89acDQzJACbjy3/lb1o5fIkErtm7A4ls5ttbWtpxqqe1xltVvmNsM/VB/8lp17QAZFnGX6bGccH2Z/Hr8WFlkb4eixVfP+QIXNh3CBo0mbV6ScImpxtvaW1XbntphdRyfSngUz6jLc4G5fZjuXAPEdWZJ7OllSTkzt4pxzqbAy6DAUCmJEwizaQOooWUSKfxd29moMWs0+G4OlooeamTJAkf7FyF96vWMvnpwf24b2SwhltFKwmDtjRv6lUpWSKBlouULONb+3cqCygdanfi412ra7xVlTkmuxgZMHPxRVQtQ9EIrnx1O761fxe8iczq4iZJhw93rsJ3Dj0SG53uon9/qN2llAV4SVUyYDlT17M9kkFbIqpTg9Ew+rOLmG6wO9GUp6xNMXpJwtHZEgnRdBrbV8jAHFGtPB+YRiCVWSz5eHcTrPqVMYNpMZ3T0YsPdvQqv98+dAD3DA/UcItopWDQluZNHbQdZ9CWlok7hg7gX9lAUoPBiC+u3gCjbmkdMntNFmX/fDHgQzCZrPEW0XIQT6dx93A/PvvKczlByK3uRnzv8KPwb+3dZe0rVr0e6+yZUiODsQimE8t/psYL/swxRYdMjUihy2xFe3ahtu1BP0Ip7qtEVDvqgd5KSyMIrGtLtHgen2JphMXw3o4efKRzlfL7XcP9uGuon+sR0IJaWhEIqkstRlXQNsGgLS19T3gn8UC2VpEeEr64ZgM8qsGJpUKSJJyQnR6Vgoyn/cy2pfl53j+Nz77yHH4+PKCUDWkxmXHZmkNx+drD0ZYNPJbrCMdM4HK5l0gYj8eUerUb7E7Y9AblPkmScGw2wJGCjOf907XYRCIiALkLmJ4wx6DtUa4G5UKTdW2JFk4klcI/smXQnHpDzkweqr53t3fj/3X1Kb/fMzKAO4cZuKWFw6AtzVuzaWbKFDNtaakbjIZx/YFdyu//r7sPGx3Fp3nXs+NVNa1YIoHmajIewzf37cR/vfoyhmNRAJkBjXe1deF7hx1Vcb1D4QjVvrXcSyS8oMpK3pLngmqral9liQQiqpXxeExZUHK11a7MAqiU02DEofbMwNxgLIKR7LmDiKrrn74pxLJ1o09q9Cy5mYFL0TvbuvDJ7pmyefeODOK2oQMM3NKCMJR+CFFxuTVtl//0Vlq+wqkkrt6zA9Fsx+d1jc14W0tHjbdqfg5zuOAyGOBPJvGc34tYOgWzjnWuqDwpWcZvxodx51A/IumUcvvhDhfO71mDVVb7vJ7/UIcTOgBpAC8Fl3nQVpU9u8XVMOv+jQ4XLDodouk0nvF7kZZl6CRp8TaQiAjAP3KybOe3mNEx7kZsD2VmUTzj8+KtrUu7T0VUj9SlEV7fyNIIi+Ws1k7oJAk3D+wFADwwehBpWcbHuvogsf9GVcRhGJo3t8EIY/bAxExbWqpkWcZ3D7yKwez05VUWG/6jd92SP+nqJUlZQTaaTis1NYlK2R0K4As7XsCtg/uUgK3LYMBnV63D1w85Yt4BWwCw6Q1Ya8vUtR2ILt+6tmlZxgvZTGKLTof12Vq+akadTpnS6EsmlEw3IqLF9IQqaDvXerYC69oSLSx/MoHnsoPCHqMJh6nKTtHCe2tLB/69Z63y+7axIfxocB8zbqmqGLSleZMkScm2HYtHeZCiJemXYweVCxW7Xo9L1xwKyzJZefV498xF15O+ySKPJAKCySRu6t+DL+z8F/ZEQsrtZ3jacOPhR+M0T1tVBzM2OWdKJLy8TOva9kfC8CUTAIBNDjcMUv7u17E5JRJYzoSIFpcvkcD27HG402xBr8U2r+frs9rgMWbKqL0Y8CGmmrFBRPP3N+8EUshce7+usYUzdGrgTS3t+HTvOohP/sHxYfxwYC/SjIlQlTBoS1XRnA3aRtNphFLskNHS8oJ/GrcfPKD8/p9969FhsdZwi6rrSFcDLNn6Vv+cnkKKnQjKQ5Zl/HlyDBdsfxa/nRiBaCV9VhuuWb8Jn161Di6Dseqvq65ru1yDtjn1bPOURhCOcc9kpbGuLREttn/6ppDO/nxCg2feA3SSJCnZtnE5jReXee1yosX2uHdC+fl1Tc013JKV7YzmNnxm1Uzg9rcTI7hpYA8Dt1QVDNpSVbTm1LVliQRaOsbjMXxr/07lIuW97d05CwItByadDkdnL5oCqSReWaaBMZq7wWgYX9n9Mr5zYLeSEWrR6fDxrj58+9AjF3S63WHZurbA8l2M7PkSi5AJTUYT1toyZSf2RkKY5PmUiBbRkzn1bOdXGkHgYBTRwhiPx5TM+G6LFaurULaK5u40Txs+t+oQpU/7+4lR3Nj/KgO3NG8M2lJVqBcjG0/wIpOWhng6jWv37oA/mQSQqb12TkdvjbdqYRyvWkxEfVFGK0dKlvFiwIfHp8bxSiSElCwjlk7hjqEDuPCV5/GiaiGwExs8uPHwo/GOti7oF3iqnbqu7YFoGP5s0Hi5SKTTSgZxo9GInhJZ/FtdM/sqa0AS0WIJp5LKAJPHaMIhttm1t+dii7MBhux55Bm/l2XUiKrkL5oFyJb6OhzLwSmeVvxn33olyPbI5BhuOPAqZznSvBhqvQG0PDSrgrZjzAyiJeKWgb3KYj9tJjP+s++QBQ9Q1cqxriboISEFGf/wTeET3avZuVtBnvBO4pbBvZhULfTlHBuETgJ82UELILMffKpnTU5t1cWw0eFS9sWXA36c2FidDK96sDMUQCydyeXf4mwoud8d627E3SMDAICnfF6c2dy+4NtIRPS0z4tkNrBwQoOnarUxrXo9NjpceCHgw1g8hoFoBL3W+dXKJaLc0ggnszRC3XhdUwv0koRv7cvM5PzT1BjSkPHZVcv3OpMWFjNtqSpajCyPQEvLwxOj+MPkKADAJOlwyZpD4VyAep31wmEwKAs+jcVj2KdaYIqWtye8k7hm346cgC2QKZUhArYGScJ72rtxw+FHLXrAFshdjOyl4PIqkaAujXBkkdIIwlqbAw3ZY9ELgWnE0+kSf0FENH+5pRGqex4QdW0B4Gk/F1kkmq/+SFjpy6+3OdBhXj5rcSwHr2lsxhdXHwp9tsrtn6fG8Z39u5hxS3NS86Dtt771LZx//vll3b5r1y68+93vxtatW3HttdfmTK9ZiPuofDnlERi0pTq3OxTADwf2KL9f0LsWa6o0DbCeqS/CnmCJhBUhJcu4ZXBv0ccYJQnfOXQLPtS5CmadfpG2LNdhDtdMXdtlFrR9wT+t/LxZFZwuRCdJSg3IWDq9bOv8ElH9iKVTSjkWp96AjY7Sx6pKqAcDn2FdW6J5e9yrKo3Q1FLDLaFCTmz04EtrNijlYf7incB1+3YiKXMwnipT06Dtrl27cNddd+Gyyy4reXs8Hsf555+PjRs34v7778eePXvwwAMPLNh9VBmPyaT8zKAt1TN/MoFr9+1EIjtA85aWdpzqaa3xVi2O41QXTf+YZqbLSrA96J+VYauVkOWcEgm1YNcbsDq7ANf+yPKpaxtMJvFqtuxDj8UKj2qAs5hjc7LSGOAgooX1gt+HaDar/7iGpqpP4e00W9ButgAAXgkGEErV9pxDtJTJsozHs/VsdQBe28jSCPXq+AYPLllzqBK4/dv0JL65dycSnEVFFahZ0FaWZVxxxRX46Ec/it7e3pK3P/744wgGg7j00kvR29uLiy66CPfdd9+C3UeVMev0cGenczJoS/UqJcv45r6dShs91O7Ex7tW13irFo/HZMYGuxNAZsGn4VikxltEC81bImBb6eMW0hGqzC6xGvJS92LQB9EtL6c0gnCka2bhnqd9U5wFREQLSj375sSG6tcUlyRJKZGQgoznVTMQiKgyu8JBjGavZTY7G9BgNJX4C6qlre4mfHnNYTBm+3VP+qbwjX0M3FL5aha0/cUvfoEdO3agu7sbjz76KBKJRNHbd+zYgS1btsBqzdRr2bBhA/bs2bNg91HlRIkEbyLOtH+qS3cMHcC/slONGwxGfHH1Bhh1Na8Ss6iOV2XbPrnMs21TsowXAz48PjWOFwO+FVlHqrHMjny5j1tI6qDtcikJoC6NsMXVUPbf2fQGHO5wAQBGswv3EBEthKScxlO+TH/AotNhSwUDTJU41j0zg+AZziAgmrPHplgaYak52t2Iy9ceDpOUue78p28K1+zdgUgqteKvVag0Qy1eNBQK4frrr8eqVaswMjKCX/3qV7jpppvw4x//OO/tt912G4LBILq7u5XnkCQJOp0OPp9vQe5zuyur5STL8oJlwojnrvdMm2ajCa8CSAOYjMfQarLUepOWraXSJurJE9OTeGD0IABADwlfWL0BTUbTsv0MC7WR491NuG3oAADgH9OTeGdrZy02b8E9MT2JWwf35ZQG8BhN+GT36gXJIqpXh9mdsOh0yrTXfJqNJhxmd9Z8XzjM7oQEQAbwUtBf8+2phheyi5DpIWGj3VXRezrW1agMMj3tm0KPZeEWGeE5hUphG1m+Xgr4EMiWKzjW1QijJFX0PZfbNjbaXTBJOsTlNJ71eZFKp6HjSurLFo8ZCyMly/irdwJAZk2C492NS/YzXmltZIvTjcvXHoav7XkFcTmNp/1efPhf/1BK9gG1u1ZJyTK2B/3wJuJoNJpwuMNV9TI5lVgpbaPc91eToO3DDz+MSCSCn/3sZ2hoaMB5552Hs846Cz/5yU/y3r5t2zbo9XqYTLmZQGazGdFodEHuqzRo6/f7oVugjD1ZlhEOhwFkAsv1yqVqc/unpmC22mu3McvcUmkT9WIoHsP/HpxZjOkcTxu6UzJ8vuWRzZdPoTbiANBpNGMoEcOOUAD9k5NwG2pyKlgwT4f8uGF0YNbtk4k4rt23E59p68GxdlcNtmzxvRQOFg3YAsD7m9oQ9NdHOYJekwUH4lHsj4QwNDUFu742C6NVw2QyjqFYFACw1mJFPBhEJUUoNuhm9ssnp8bxBsvCLZbIcwqVwjayfD0+Maz8vNlkrbhvVEnbONxqw/PhILzJBP41PorVXPF+2eIxY2G8GA7Cl637f6TNiUQwhKV6NbMS28gqABe39+KbwweQhJwTsAVqc63ydMiPOyaG4VXVGm/UG/Ch5o6aXS+tlLaRLrNERk2u1EdGRrB582Y0NDRkNsJgwIYNG3DDDTfg+OOPn3X74OAg3G43du/enfM8oVAIRqNxQe6rlMvlgn6BLi5FBN7tdtd1o+2OhQB/ZnpVxGSsOPBN5VsqbaIeRFIp3LhzL6LZkh2va2zGv/WsXvafW7E2clKoGfeNHoQM4BU5gTe6l0/maUqWcdfA7qKP+fnUKE7p6K7pCPJi8CUSuLV/l/K7TadHOJ1Sfm82mvCJOss83hJoxIHxYcgABvXAcUv4PPLU5Kjy89ENnorPiW4AnWODGIpFsTsaht5uh2OBBlh4TqFS2EaWp7Qs47nsOdMoSTi5vQvWCq9nKmkbJyRa8Xx2ccad6QSOdLfPYatpKeAxY2E8Mz2m/Hxaa8eSvt5eqW3kWJcLtvFB+IssAvyziWG0Ol2w6vUw63SZf5IeJp0OJp2uatcwT0xP5k108aaSuGF0AF9avaEm1wkrpW2kUqnSD0KNgrbt7e2IxXIXqxoaGsJnP/tZPP7447NuP/7449HX15ezSNjg4CDi8Tjcbjc2bdpU9fsqJUnSgjYo8fz13GhbVKtiTyTidb2ty8FSaBO1Jssyvtv/KgazC26tstjwH73rFiwrvt4UaiMnNHhwX7ZUxD99XryppaMWm7cgXgn6c0oi5DORiOOVUACbnEu3o1uKLMv43sCr8GazMY5yNeDLaw7DjlAAU/EYTPEEtrZ1wFBn+8IRTjf+bzyT9fVS0I/j6yigXKkXVHV5j3Q1zOlYfay7Cf83NoQ0gOcC03jdAtau4zmFSmEbWX5eDQcxlT1nHulsgG2OA0Plto1j3U3AQGbm07P+aZzT0Vv08bS08ZhRXbF0Slk00K7X4xh305L/bFdiG3kl6C8asAUAfyqJr7z6csH7jZIEs04V0NVlArrq39XBXu3tJp0ORknCD/qLr+f0o8F9OL7BU5NEl5XQNsp9bzW5WjvllFOwZ88e/PznP8fIyAhuu+02vPLKKwVvP/nkk7F161YEAgFs27YNAHDzzTfjpJNOgl6vX5D7qHLqoO14PFbkkbQcLIVFnn45djCnc3PpmkNh4f6NdTYHPNmFp14ITCOcKt5xWEq8JQK2lT5uqfrtxAie8mUWenEbjLhw1SEw6nTY5HTjdU3/n707j4+rrvfH/zqzL8ksWbukSZs23dKN0gIFLgKWyxUFQS4q6r3gFQSXi0q5P0EF0aIWl0u9gGJBv4ICBaEWEb0CihfFAi1L6ZYmzdakadqsM5l9O78/5szJmTbLJJnlzMzr+Xj4MOlMJifhkznnvM/7vN6VWGa2qrLTeHmJDYmtOpDHw8hioijn0Zo1WjRYpxdtsJ6DeyjHlPv6Q36vKvf1NH27hgbkj89xZv4iWaXBiDqTBQDQ7B2BSxo4TUST2+MakiOvNjjKYVDZhXdKTTrOQcKiCE80goFwPIqr3e/FYe8I3htxYbdrCH8f6sefB07iD329+O3JY9je24VHezqxrbsN9x89gh91NGNL+2G4JzkH7A+HcNCjjgi1YpaTTluHw4FHHnkEW7ZswZYtW1BRUYH77rsPjY2NY/57YljY5s2bsWnTJnz/+99HNBrFr3/96/gPodOl/TGaOhZti8euoQE83N122pCnG2vqsSELB/2peG9kGL861il//pX5izE7g4N88okgCDjHUY4X+o4jIorY4xrKaAdfNjn1hsmfNIXn5aNOvxf/r7tD/vyWukV58/OW6vSYb7ai3e9Fm98LTySSsUiATOr0++TMuRWlNuiE6Z1YLbPaYNZo4Y9F8ZZrCFFRVGWxnQrTmPv6/h5V7etp+kRRxC5XvGirAXCWvSwr3/dMuxOdAR9EAO+4h3BheVVWvi9Rvvu/wT754wuchXHcXoxSPSY/31EBm16HYCwm/S8qfxw65fNgLHpaPm667DxxDDFRxPISG/S8UJATOTsTWrNmDbZv357yvwPAxo0b8eKLL2Lfvn1Yu3YtysrKMvoYTY1dp4deEBAWRfSzaFuwdg0NYEt702n/PhAOYUt7E27H0pyfzPWFgvhB+2Ekor0/OqsG67N0MpIvznGU4QXpNvQ3XIMFU7RdXmJDud4wYUSCU6fH8pLCHEQWisXwo/ZmhKQM5w9Wzo7fjppHVpTY0O73xjOXve68/Nt9d2RY/nhNqWPar6PXaHCGzYF/DA9gJBpBs3cEywp07ZK65MO+nmam0+9DrzQscUWpHTbd1Gd6TMeZNid2SBFNb7FoS5QSTyQi33Hj1OmxooAjvgpdKucqFXoDbl2weEoX6qOiOGYxN5T4WEz+vNPvw0uK+Qvj2eMewh73EIwaDVaW2LHW5sQZNgdmG00FHV2gJnnXvlJdXY3q6uqsPUapEwQBlQYjeoIBdtoWqKgo4uHutgmf80h3G85ylOWsGywUi+HetiY5K2itzcHMtDE0lthRotXBE43gLdcQwrFYQVw91QoCbqypH7PYkCBAQCAWhVWbd7vAST16rAOdgfi01TqTBdfPnZ/bDZqGxlI7nk/k2o648rJou1dRtF09g6ItAKyzO/EPKeZlj2uIRVvKuHzY19PMve5SRCNkcSDp0pJSWLVaeKNRvO0e5h0ERCnYNTyAiNRJeX5ZBf9m8lgq5yo31NRP+b+xVhBg1mpTHiYZFUW87R6adBZIQjAWkwu4ADDLYMIZNgfOsDmwstQOSwGeV6lF/p+hk6pUSBEJvlgUnkkCtvNNPmS4ZtrBFIc8favlAJ47cQzvjQxnfR083NWGFmkycbXBiFvnT+0qZbHQCoKcl+mPRZOKTPmu3mLFWP/FtdK/DkZCuK+jGbEC+xve4xrE76Vip0HQYNOCxXmZd9aoKEru8+Rfrm04FsOBkXj+V5negJoZxrKcaRvNtd3jHpzRaxGlItV9PXPu8lsi8x8AznZk7+KYTtDIdyB4pDsIiGhirw6NRiO8j9EIeW+Dsxy3L1gqzxhJqNAbcPuC7NzJkigeT+SWukXYNH8xLi6rgvOUuzF6QwH8sb8X321rwr+99ya+3rwPz/R2o83nKbhzrFxjOZzSSplr2x8K5mUW4VjyIcM1G1INTt/rcWGvothSbTCi3lKCerMV9RYrFlpKMpKx+VL/Cbwo3eZhEDS4vX4pSrN0u18+OsdRjlekfKw3hgfz7jb68Tx3sgeJQ4WLyypxhs0Jp96Acr0B/3X4PXiiEex2DeGp4124dk5hdGEPhUP4n84j8uefrpmPOrM1h1s0fTadHnUmCzoDPrT7vPBGI3nVFX3I65bjKdaUOmZ865hDb0CDpQQtPg86/D70hYJJ+1qidBBFEe1+L950DeLP/SdT+ppCH+hYyI4H/Ojwx+/KWGwpkZsusuVMuxOv8Q6CtIuKIg563BgKh+DUG7C8xMbGhQIwEApinzTcdLbRhEWW6Q03JXXZ4CzHWY6ynP7NbnCW43YsPa3OUaE34AZFneOCskqIoogOvw9vu4fwjnsYh7xuufs7IorY73Fjv8eNX/V0wqHTY43NgbU2J9aUOmDX83x8JvLnLIjyQqVeMYwsHMR85GfRQIm5bqOmW2g9EQriRCiY1NXh1OnjhVyLFfXmeCG3ymCcUoFDeXDqjUbwSNfo7Zyfr12Ieh7UTOgMmwMGQYOQGMMbrkHcXAC3KLojYbwsFe6NGg0+XbMgKafvtgWL8e0jBxEDsL23C/UWK8525Pffb0wU8ePOFnnw1Xq7Ex+omJXjrZqZFaV2dAZ8iAE45HHn1QWFve7RC1ar05Q5t87ulO8g2OMaxAcqZ6fldam4RcQY9o+48aZrELtdgzg5xWirfBlwSKfbpYhG2JCDfaDyDoK33EP4t7l1Wd+GQsMGk8L196F+uRnhAmclc0QLiFYQsDLH+cSpFo8FQcACixULLFZcPasG/mgU+0ZceGdkCG+7h+WMdAAYjoTx18E+/HWwDwKAhZYSOUphibV03AG9iXP7wVAQhlAY62026LjeWbSl9FJ2/xRCri1z3ZKlEpxerjfga/VL0e73oc3vQZvPi3a/F8FYLOl5Q5Ew3nIPyaH6AGDValFvlgq5FivqzSWYazKP+bsd6+A04bLKWbiIgy0mZdRocYbNgTdcg3BFwjjsHcn7AV1/7OuV19rG8urTBqucYXPi3+bU4dGeTgDAfR0t+OFSM2pMlqxva7r8vu843nEPA4hfDPnP2oa8P6BfUWKTB+XtH8mzoq0yz9bmSMtrrreX4cnjXQCAt1xDLNrStHmlHPPdrkG85R6CNxod83laCIhi4tsb/zJwAnVmS9YGWOVaIXUxvj48GrVyTg6Ktg69AYssJTji86Dd78VAKIhy3kEwbWwwKWyvDvXLH19QVpHDLaFCNZ3isVmrxVmOMpwlxescD/jx9sgw3nEPYd+ICwHpfEwEcMTnwRGfB7/p7YZFo8WqUjvW2p04o9SBKqMJwDgXnvp7eOEJLNpSmlUUWNF2Krluub5Klg2pBKffWFOPRdZSLLKWAogP+IuKIo4H/Wj1edHm86DN70WbzwtPNDnv1huNYp/HlZRjadRoMN9slaMV6i0lOB7w44cdzeNuw3Jrfhces+kcRznecMVP3t4YHsjrom0wFpULfRoAH66aM+bzrqqeiyM+D14bHoA/FsV3W5vwg6Wr8uoW/IQ2nwePHuuQP//y/MUFcQtSY8no++n+PMq19UQiaJU6YutMlrR1ItabrXDq9RgKh7F3xIVgLAqjJrVBE0R9oSDedA3izeFB7Pe45NsZlXSCgBUldpztKMN6exmOeD0T7usB4C+DfdjtGsL1c+fj4vIqaPK0gJmKQupiHAgFcVjKka0zWTBnhrnb03WmzYkj0vvlW+4h/HOe3yGSK2wwKWzHAn7572Sh2ZrXTQZU2GabzPigyYwPVs5GOBbDIa8b77iH8Y57GO1+r/w8XyyK112DeF06/6wxmjHLaMQeqQFFiRee4vLvDJVU7dRM23yXal5bMeW6bXCWo77XijbFmy9wevaNklYQUGOyoMZkwfvK4uH5oiiiLxREq18q5Pq8aPN7MXjK7zIYi+Gwd0Q+wUjFL4914FwnJ6umYr3dCQ2AGOKdN9fPnZ+3XZp/GTgpRwSc56xAtXTl9lSCIOCWugZ0B/zoDPhwLOjHfR3N+Fr9srwqOgRjUfyovVkuwFxZNQdr0tTZmWt2/WiubavPA180khdTafeNuJC4p2CVLX0X8gRBwDpbGV4aOIGQGMO+EVdedR9Tdinzad8YHjxtf51g1Wpxps2Jsx3lOMPmSLpwVWkwjplzV6434EybE68N98MbjWIkGsH9R4/gz4Mn8bl5C1FrLryCQqF1MSYu1AK56bJNWGd34qne+B0Ee1ws2k4XG0wK298UA8guKOMAMsoPeo0Gq0odWFXqwHVzgcFwCO+6412477iHMaJo3OoO+tEd9E/4esV+4Un9Z0CUVyoMo11FhdBpm2qXVDHluomiiP5w/L+tWaPB5+YtRJnBOOXbBAVBQJXRhCqjKSlPbTgckgu4rVJXrjIjJxU8OE1dqU6PFaV2vDfiQm8ogE6/D/Mt+ZdFHRVF7DzZI39+VfXcCZ9v0mrxtYXLsKlpb94OJvt5d7t8kLPQbMWn5hRWJmBjqU2RazuCM+3OSb8m15TRCInp6Omyzu7ES1Je8x7XEIu2lCQci+GAJ55P+6ZrcNxjsCqDEWfZy3C2owzLS2zj5soByTl3cr5c9WzoNBp8Yk4tft7djr9Jt+0e9Ljx5UPv4srqOfjY7HkF0wleiF2MyvkCucizTVhkKYFdp4crEsbekWGEYzHoNeOvRxobG0wKlyiK+D9pYLAA4HwnoxEoP5XpDbi4vAoXl1chKopo83nwtlTEbfKOTBLGxHN7Fm0prYwarXwAVghF21QyXCukXLNicTIUhDsSvzq2rMSG96U5O9ahN2Ct3YC1igKNNxpBu1TI/cdQPw6l0HXLg9PUnWMvx3vSVNrXXQN5WbR9Y3hALu6vKrVjYQpD6GYZTXk7mGzX8AD+1D86cO3WBYsL7mR3RYkdf+jrBRCPSMiHou27UtFWCyEp4iEdVpc6oBMEREQRe9xDEEUxb7viaWKpZqcm8mnfdA3i7QnyaRdarDjbXo6z7GWYb7ZMad0kcu5EUYTL5ZK3w6k34LYFS/D+8io81NWG3mAAUYh49sQx/G2oHzfNqy+ICwsHRlwF1cXojoSxX9rfzzKYMD+HndEaQcAZNgf+OtiHgHTRoVDuFskmNpgUrla/Fz3SsW1jiS0phpAoX2kFAQ3WUjRYS/Gx2fPwYl8vHuxqnfTrivncnkVbSrsKgwGuSBiD4RCieT6NPpUM1xtq6vP6Z5yqRK4SEO+SyAarVocVpXasKLVjgdmKb7Tsn/RreHCaurMcZdgmdRK9PjyIj8/On25TIN6JsOPEMfnzj0zSZauUj4PJBkJBPNh5RP78hpoFqt7e6WpUXAxLFBnU7EQwgOPSydWSklKYtentNDRrtVhRYse7I8PoCwXRGfBhvjn/LrDQxCbLTu0LBfHm8CDecA1g/4h7zGFhOqnQerY9nk+byRP9M2xO/M+yNXimtxs7ThxDRBRxMhTE5tZDONdRjhtqFuTlgCl/NIr/G+zDb6Tb9yeTLyeTu12DcoTLOY6ynF/4WWdz4q9SJ+Fb7iEWbachlQYTgyCgIUvH7JQ+rw6ORiO8j9EIVKBmp5irXszn9izaUtpV6o1ohRcxxPNLKvPwYF1pg7Mc1wbmyZO7E0q0WnyxtiGvcszSoVnR5dpgKc3692f3c/pVGoxJU5xPBAPj5sGq0QGPGy3SxYQFZuuUb0s/dTDZd1oP4YdLV6tyMFlUFHFfR4ucBbXBUY5LyqtzvFWZ4dAbMM9kRpc0hMMfjaa9EJpOmYxGSFhvd8rdvHtcQyzaFpjJslOrjhlxcpy7mKxaLdbZ4rEHZ9gcWc2ANmq0+OScOryvrBI/PdqK/R43AOAfwwN42z2ET82pw2WVs/PiAneX34c/9vfiLwMn4Y+N3bk8lnw5mXx9eDTPNpfRCAln2EZz9d9yDeEzNQtyvUl5J5UGk5AoYkt7E+6oX1ow0SWFLiqKcvyMThBU8fdKlAk8t59cYd1LSaqgLNIWQkQCAJjHOMDZYC8vuoItkNxp22DN/lX7xMHpRIqt+zkdznGM3sb6uiLvLh/8VtFle2X1nCl3DiUGk9VJ3ao9wQDu62hGbIwJ67m288Qx7PPEu07L9QZ8oXZhzjulMmmFFDEQA3DI687txkxir6IbeHWGbpNW3m6+RzFMiPJfKtmppxZsqwxGXF45G5sbGvHYqrNw64LFOM9ZkbOhfTUmC+5pWIEv1zXArtMDAAKxGB7pbsd/Ne1FyxQGimZTRIzhtaF+fKN5P7546B280Hc8qWCrm+Q9tjxPTib90SjecQ8BAJx6PRZbs3/h/VQlOh2WWuO/u2NBP45PMoyGxrbBWY4rKmef9u82nU5ev++4h/HtIwfhUwwAIvU64HHJw5nX2pwold5TiQoNz+0nN+WibSwWQ3d3N9577z0cOXIEHo9n8i+iolKIRVtloVL+t3GmMReyqCjKv4sKvSFnnSUbnOW4fcFSlJ/y/Sv0Bty+IL+mOKvFOfbR39kbeVQMOur3YY90ElqhN0x7SENiMFmJVOxIDCZTkxbvCB7vOQogPpDi1vmLC/4gfoWi+KnmiISYKOI9qQPWotGiIUPFkFlGE2qM8dvIDntH4I6EM/J9KPtSmQAPAHOMJnxydi1+vGwNtjWeiRvm1WNVqWPCgWLZJAgCLiqvwoPLz8A/K+4CaPV78V+H38O2rjZ4VVI0GggFsf34Udy4/y18v/2wfEEMAAyCBpeUV+O+patx2/wlE77OIktJXpxMvu0eQli6GHmOvRwalWyzMq/8LddQDrckvxkUDSYfrpqDexpW4Jcrz8K3Gxrl5pP9HjfuPnIQnog6/gZpfK8O9ssfX8ABZFTgeG4/sZQvxXd2duKhhx7CW2+9hZqaGjidTni9Xhw7dgwOhwMf//jH8cEPfjCT20p5ohCLtm2+eIFWJwioNphwLOhHp9+LYCxaVLcZHQv4EYjF09AyVZRIlXKq9WTDWmhy88wWzDWacSzoxyGPG65wGHa9+guCyi7bD1fPnVHhQs2DyfzRKH7U0SznV15dXZNU0CxUSbm2HvV22nb4vfKAxpWl9oy+D62zO9F90o8Y4p1TzLkrDKlmol47uxYX5MF/81KdHl+oW4SLy6vw06Ot6Az4IAJ4oe84/jHcjxtq6nGeozzrdwqIooj9Hjf+0Hccrw8PyPmuCXOMJlxWORsXlVWhRBc/Taq3lOB2LD0tazhht2sQTR43lqq823aX4i6ac1SwX0tYZ3PiV1Ku/FvuIXyoak6Otyg/dSqaST5YOVuOuWossePbDY24+8gBeKNRHPaO4M6W/fhWQyNsBX7hN1+FYzH8YzhetDVpNDjLkf9DHYkmozy3HwwFYQiFsb56NnQFNmh5OlIq2j7wwAN4+eWXccstt2Dz5s3Q6ZK/rKurCz/5yU/w5JNP4r777kNlpfoPJilzCq1o64tGcEy6XavObMF8sxXHgvET5nafV/UH6emUiyFkE0lMtab0ONtRhh0njiEG4E3XIC6pUHdWan8oiFeH4kMaSrS6tGS7qnUw2cPdbfKQqwZLCa6dMy+n25MtTr0BNUYzuoN+HPF6EIhGYVJhru27ijzb1RnKs01YZy/DzpM9AOIRCSzaFoZCnQC/rMSG/162Gr872YPtx7sQjMUwFA7jB+2H8WebA5+dV4/ZxtSGkMyELxrBK4N9+GPfcXQFkm/B1wA4y16GD1TOxqpS+5gdqGNdKD7kcePx40cRA/DjzhZsXbZGtRfyw7EY9khdrCVaHVaUqufYtc5skfMM9424VPs+r3adAR+AeJHv1Hkii62l+E7DSnzzyAG4ImG0+b34evN+fLuhMe/eU4rB2+4heKPxiJZzHOWqfV8hSrfEub0oinC5XGzGkkxatv7Zz36GEydO4JlnnsHFF198WsEWAObNm4fvfe97+I//+A/853/+J0QV5gBS9hRa0bbD75NnMy80lyRNXx0rNqGQtfiUQ8hyX7Sl9FJ23ryRB7m2z5/sQUTa3/xLxay0Dam6qnquHLOQGEyWy9t5/zbYhz8PnAQQPxnbtGCxam6FzoZGqbgQhYgmlWZi7nUr8mxtmb2QtKykFFZprb/tHkaUx1wFITGIYyL5OohDJ2jwkeoaPLDsDKxX3Ar/tnsYtxx8F08f70I4dmrPa3p0+L346dFWfHrfbmzraksq2Dp0enx0Vg22rViHOxYuwxqbY8LIgMTJ5AVllVhZasfVs2rkY6GeYAC/luJr1GjvyLCc03uWvUxV+xBBELBOWhdhUUyKqaDU+KIROfO6zmwdcx0vsFjxnYYVcEp3UR0N+PC15n0Fca5WaP5vsE/++AInL8wSFbtJ99g33HDDmN21Y7n44ovx+OOPF/RQFJqcTaeHXloD/QVwINCqKMwutJQkdZi2FFvR1pv8u6DC0mApQZlUNHh3ZFjVwyq80Qj+1H8CAKAXBHyo6vQBHNMlCAL+s3YR5psVg8naczOY7GQwgJ92tcqf3zRvYVa60tQkMYwMAPapMNc2FIvhoBTdUK43YG6G//voBA3WSN28nmgETSof0Eap0QoC/nVWzYTPyfdBHFVGE75evwx31I/m1oXEGB4/fhRfPvRu2nKrw7EYXh3swx3N+/ClQ+/if/t75WgnIF4g3zR/MR5ZsQ6fnFN3WldiqrSCgC/Pb5CPeZ8/2YMDKi04JkcjqO9W6zNtzLWdiU6/T/44cewylnlmC77XsFJe8z3BAL7WvA+90p08lHu+aAS7pb8Bm06X8QvBRKR+kxZttYrOpa6uLrz33nvy57FYDH/+85+xY8cOtLS0nPZ8Kk4aQUCFdDBQCFdvk4u2Vsw3W+VJrEe8xVO0DcdiaJfysuYYTXLWGxUOjSDgbGk6fVgU8Y57OLcbNIE/9fXKXUMXlVWl/fY+k1aLO+oVg8nc2R9MFhVF3NfRIt8id4GzAhcV4a3wyuxeNRZEmrxuhMR4QWh1qSMrF67X20eLLntY4CgY4929U0iDOARBwDmOcjyw/Ax8uGqOfCLSHfTj6y378eOOFrjC0xuw1xcK4vGeTtywfw9+1NEsX0wB4ncp/EvFLPx42Rp8b/FKXFBWCX0acvJqTBZ8ak4dAEAE8D8dR+CX3rPVIiqKeFMaMGrUaLDG5sjtBo0hPkwv/t65xz3EuzanSFm0rTNZJ3zubJMZ31u8ErOkzNuToSC+1rwP3QHfhF9H2fHG8KB8THGeo0JVXfFElBspvwv84he/wIc+9CE8//zzAIBoNIrrr78ed9xxB5566il84hOfwK9+9auMbSjll8QVXF8sqpopwdPVKg0h00JAndkKvUaD+eb4AdGxoF/V3Yjp1OH3yreiN1hyO4SMMkcZkfC6SiMSwrEYnu87DgAQAHy4OjNDSxKDyRI7yu29XVmNjXimtxsHpS7KKoMRN9cuLMo7Wcr0BsyRTi5bfB4EY+oqiCijEbJVDFlrcyKxEli0LQzHA368IsWgWDQafL1+GTbNX4x7GlZg24p1BVGwVbJodfiPmgX40dLVSXFLfxk8iS8cfBsv9Z+Q726IiiL2jbjw6mAf9o24kiJBYqKId93D+F7rIXx2/x483duN4cho0bfGZMZn59Xj/61cj8/VLpSP39Lp8qo5WCYNZ+0NBfBYT0fav8dMHPS45UGJZ9qcqszHNGu18l0VfaHgabnDNLHOwOgQsroJOm0TKg1GfLdhBWpM8TtDBsIhfK15PzoUw8woNxKzGgAws56IAKQwiKynpwfd3d342c9+hi9/+ctobGzE7t278ac//QkHDx7E5s2bUV5ejoMHD+KBBx7A0qVLMXfuXMyZw8mfxaxCn5xrazXnZ1dmMBaVrzzXmi0wSF0ZDZYSHPF5ICLeGbMqw4Nn1EDZAdRgZTRCoVpRaoNVq4U3GsVb7iGEY7G0dCOl0/8N9WFQmuB9tr0so0PCcjWY7JDHje3H4/mIGgC3zl8MqzY/30fTYWWpHT3BACKiiCbPCFarqFMseQhZdm5jtOv1WGwtxWHvCI4GfDgRDMiTwik/PdXbhcQN/FdV1xTNtPB6SwnuXbIKL/b34lc9nfBGoxiJRvDA0SP4y8BJbHCUY+fJYxiQ3vOBeAzJv82pw0g0gj/2HUfPKbd2axC/AHlZ5WysKLFl/GKXVhBwS10DvnToXYTEGP7Q14tz7OWqeZ9SRiNscKi3+H+m3Sm/n+5xDaI2heIjxSV12qb4eys3GPFdaThZu98LVySMbzTvxzcXLUeDlc0ZuTAcDuFd6S63KoMRS/jfgYiQQqdtd3c33nzzTQSDQfT09ODNN9/Er371K2zfvh3XXnstWltb8eabb6K7uxt+vx9vvPEGjh07lo1tJxVT5oPlc65tu88rn0QttIx2ZyyyFt8wMmV+L4eQFS6doME6W7xY4I1GVTcQJCaK+O2J0X3MVdVzM/49sz2YzBuN4L87muX3no/NnodleTh8KJ0aFbm2+1W0JkciYTlCp85kgSOLU7jXKTIg97jZbZvPugM+efBMqVaX1ozufKAVBHygcjYeXL4WF0jvtQBw0OvGz4+1JxVsgXhX4NbOFvy8uz2pYOvU63Ht7Hl4ZMU6fLV+KVaW2rN2d8IckxnXza2TP7//6BFV3IkliqJ814xOMfBLjZTvaW/xPS1loijKRdsyvQGlOn3KX2vX63FPwwr5uH4kGsFdLQdwyMOs9Fx4bXhAPvb7J2fFhIMRiah4TFq0Peuss/DFL34RV111Ff7+97+jubkZu3btwmc/+1ls2rQJ11xzDXQ6Hd5++21ceuml+OIXv4j169dnY9tJxZRF23zOtW1V3CZUryhUKouWxZJrm/g5tRCwwJL+2wtJPZRDSt4YHszhlpzuLfcQuqXbJpdbbViahWJmtgeTPXS0VZ4CvcxaimtmzcvI98knKxT/nfer6GRy34gLiVWQ7ZxIZa4tB/fkt6eOj3bZXlk9F5Yi7ap36g3YtGAJvrWoEbOmMBxsZYkd/9+CJXhkxTp8fHYtyqc5WGymEp29QPzY9/91d+RkO5SO+Dxy0Xt1qUPVa2uOyYzZ0h0DBz3uvI9Xy5bBcAge6XeVapetUolOh283NGK5tHZ9sSjuPnIA7ynuIqHsSFy8AxiNQESjUr7n9c4778TnPvc5zJ8/H/fccw9uueUWAMDQ0BCamppw9dVX4zvf+U7GNpTyS8EUbRXdpYsUhdoakwVG6ZbxliLotA1Eo+hSxESoMQ+N0metzQmDNPjgDddAxoqT07Ejy122CdkaTPbXgZN4dagfAGDVanHr/MV5PS0+XcoNRvlkvtk7oppc2+RoBEdWv/d8swXlUmfveyPDCKhs+BGl5qjfh79Jf/M2nQ4frCyuLtuxrLE5cPO8hSk995baRbhn8Qqc58z9wB6NFJNgko4PXxw4gbdz3DGqjEY4Jw8iN86Uum1jgHybOE0seQjZ9CIlLFod7l60HGuk/VggFsPmI4d4QTCLeoMBHPaOAIj/d6zLQP42EeWnlI9uNBoNrrjiCtx666249NJL5X9funQptm7dik9+8pMwGLJ3WyCpW4ViLeRz0bZNGkKmAeQuOyB+K99Cc7yIezIUnPak43zR6vfIXUCMRih8Jq0Wq23x29GHwmE0SweRuXbYOyJPA68xmrN+m+csown/tWBJxgaTHQ/68VBXm/z5zfMWooo5pbLEkJqIKMonNrm2dyQe1aATBDRmOcJCUNzqHBZFvDeintgISt3240flbu2rqufCrOVFUSB+m3Yq1Ja5Xm004dNzF8ifP9B5BJ5IbjpGRVGUi7YaAGfZ1V+0Ve7XOWQxNR1JQ8imX+gzarT4+sJlWC/9NwiJMXy37VBS4Z8y52+KAWQXsMuWiBQmPdJ5+OGH8bOf/SylFztw4ABuuOEGiCrqyqLcSM60DU3wTPUKxWI4Kl29nmc6vbu0mHJtlREQHEJWHM5RDCt5w6WOiIRTs2xzkfW1xubAv80ZzS28r6NFHlY4ExExhv9ub4Zf6iC9uKyKB+2nSIpIGMl9RMKJYAC9Up7mUmspTDkotiXypwFgj1sdf6eUug6/F/+QCiJ2nR6XVbDLNsGZYj50qs/LpksrquWOxYFwCD/vbs/JdnQF/HLm7/ISW1Yzt6erscQu38n2tntIVXf6qJWy03b+DIe3GTQafHXBUpwnHQNGRBHfb2vCq4rb9in9RFFMikb4J0W2NxHRpEXb6667Dh0dHfjMZz6D5ubmMZ/j9/vxwAMP4Pbbb8dXv/rVrIX+k3oZNVrYpSD8vnB+dtp2+r2ISv0v9WNkuCo7Tlt86uj6ypTmcWIiqHCdZS+TdxCvDw/k/GLcsYBfHqbi1OtzmvWVicFk2493yX9ns4wm3DhvwSRfUXwaS9U1jGxvDqMRElaV2qGXjrn2uIZy/ndKU7P9eJfcZXt19dycFP7VanmJTY7/GE+F3iDncKqJIAj4Yt0iWKSL/X8ZPIk3c5AP/3pSNEL5BM9UD4NGg1XSe/2wYtAjjS9RtNUgHt82U3qNBpsWLMGF0nFWDMB/dzTj5f4TM35tGlun34cuaV7DMmspqnmXFREpTJpGbzAY8L3vfQ+7du3Ct771Lfh8PqxYsQJlZWXwer3o6upCR0cHPvzhD+OZZ56B0Zib8H9SnwqDAa5IGAOhIKKimHe5jMru2YVjFCqThpEV+EFlotPWIGhQO8Or+JQfbDo9lpfYsN/jRk8wgK6AP6f/7Z87eUwublxeOSent8QmBpN1B3zo8PvkwWRfW7hsWt2/+0dceKa3G0B80N+m+YtVPSwmVyoNRswymNAbCqDZO4JQLAZDDteBMm9xdZaHkCWYtFqsKnXgLfcQBsIhdPh9HBSZJ9p8Hvm2Y6dOj3+pnJXjLVIXrSDgxpp6bGlvGvc5N9TUq/bYstJgxGfmLcD9nUcAAD85egRLS86ATWpoyIZdeVi0BeJ3EOyWohHecg+hwVqa4y1Sr6goyjMn5hjNadsnagUBX6prgFGjwZ/6T0AEcP/RIwjGYvhgFe8ISLf/YzQCEU0g5Xf2DRs24PHHH8cjjzyCyy67DIsXL8Z5552HW2+9FX/605/w+c9/ngVbSlKpj6+HGOKTTfNNIs8WGLu7dJbRBKvUFXPE6ynYDid3JIzeUPz2unqLNeeDPih7lCd5r+cw02w4HMJfBk4CAMwaLS6tyH1xY6zBZNunMZjME4ngvzua5YL0J+bUYjFPUMe1ojTeVRfOca5tTJEha9Vqc3oHwpmKDMjdKokyockp3y+unlXDAZ9j2OAsx+0Llp7WcVuhN+D2BUuxwanuQuT7y6rkwVpDkTAeVmSWZ9qJYABt/vhx7CJLSVJsmdop39M4CGtixwJ+RKTzj7o0X1jXCAI+N28hLlcUabd1t2HHie60fp9iFxNFeRilBsB5DkYjEFGyKVdfysvLsWHDBnzwgx/ERRddhCVLlmRiu6gAKA8Q83EYWeKWLAHA/DGC/QVBkE/UhyJhDORhYToVyi5iDiErLmcrhpa87spd0faFvuMISycll1ZUo0Snji7UUweTPTXFwWSiKOLBo0fk946VJXZcVT03A1taOBLDyADgQA4jEtr9XnlQ0ooSe067/dbZFIN7cjypnlJzxOeRs8LL9AZVXIhSqw3Ocjy8Yh3uaViBTfMX456GFdi2Yp3qC7ZA/DjxC7UL5Qv8rw71Y9dQdvalygutG/KoyxaInz/USbf5t/g8BT/sdyY6/ekZQjYeQRDwmbkLcM2sGvnfHj3WGR+gWKDNKtnW5B2Rz5PPsDlh12evG5+I8gNb5ihj8rloG47F0CndbjTXZB53mnODZbQjrqVAIxKUQ8gWcQhZUakymlAvnQS0+rw5+Tv2R6P4Q18vgHh0wOVVc7K+DRNZY3Pg3+ZObzDZywMn5SFEJVodvjy/QbW3+qrFCkWu7b6R3BVt96ogGiGh2mhCrVTgaPaOsMCRB7b3HJU/vmZWTU5jPvKBVhCwstSOC8oqsbI0txdJpqrcYMRna+rlz3/a1ZqVv9HkaISyCZ6pToluWxHxgWQ0tk7F8Ua6O20TBEHAp+bU4ZOza+V/e/J4Fx7r6WThNg2UQ94u4AAyIhoDjxIpYyryuGh7NOCTbzdaaB6/UKksYiqLm4WkJanTlrdtFxtlRMJUukjT5eWBE/BIHY0XlFUkva+oxVVVUx9M1h3w4eHu0Vtlv1i7SJU/m9pUGoyoln5PzV4PQrFYTrbjXcUQsjU5GkKmtK4AChxRUcS+ERdeHezDvhEXogVaDGj2jmC39N+oQm/AJeXVOd4iyrT3lVXKd664ImH8tKs1o8WuoXAITVJ8zDyTOS3DqbJNeQfBW3n6npYNiSFkQOaKtgkfnT0Pn6kZHZK648QxPNzdjpiK36vVvl+JiDH8XYpGMAganJWHF1iIKPPUcY8pFSRlp21/nhVtW5OGkI1/u1ExDCNr8cUP/K1aLWZzmmnROcdRhieOx7vCXh8exIey2OkaEWN47mSP/PmVKo0OmOpgsnAshv9ub0ZQKjheWlGdF7f6qkVjiR0nBk8iJMbQ4h1Bo6L7NhuCsSgOetwA4kW3OSp4X1xnd2LHiWMA4rm2F5VX5XiLpmbX0AAe7m5Lihkq1xtwY019wf1tPHl8tMv2o7Pn5XSoImWHIAj4fO1CHDzoxkg0gl3DA/jbUH/GBg69MTwo56Tn0wAypSUlpbBqtfBGo3jbPZSXA42zIRGPYNJoUG3I/L7oiqo5MAga/LSrFUA8vioYi+LztYtU998nH/Yr77qH5ailsxxlHEJLRGPikSJlTD7HI7QqhpAtnCDHtVxvgFOaBHzEV3jDyAZCQQxJt/EttJSMWYCiwlZrsmCWVJQ64HHBHcnerdevDQ3I7x1n2pxjZkurxVQGkz3ecxSt0olWjdGM/5i7YMzn0dhWKoq0+6XiaTY1eUbkjOU1NgcEFbwvLrXa5LX37sgwImJuOpCnY9fQALa0N52WCz8QDmFLe1PWMkCzocnjxttStEaVwYiLy/KruE7T59AbcHPtQvnzbV1tGMrQLIRdeZxnm6ATNPJdDN5oNKeDJ9XKF43ghHSMVGuyZO0Y/V8qZ+FLdQ1yEeHlgZPY2tGsqv1OvuxXXh3slz9mNAIRjSfnRdsf/vCHuPnmm8d87DOf+Qx27Nghf97c3Iyrr74a69evx7333ptUIMvEYzQzdp0eOukAIv+KtqNds/UTdNoqh5F5ohH0BgMZ37ZsauYQsqInCALOkW7rjAHYk6VJzqIo4rdS5yCAvBjQNdZgstdPiZR41z2M356M/1w6QcCmBYthGiczm8bWWGKTP96fg2FkymiE1SqIRgDimZ9rpWxdbzSKQ578KHBERTEpJmQsj3S3qe6W1ul6UnEh56Oz2GVbbM53VuA8qYg6Eo3gJ0fTH5PgiUTkvO8qg1HOpc9H6xTDUN/K0rFHPukK+OWPMzGEbCIXl1fhtgVLoEX8PO/VoX78oO0wwjmKLFKKiiK25cF+JRCN4g3X6FyDtYpIECIipZweLTY3N+OJJ57A1772tdMe+93vfoe///3v8uehUAg333wzGhsb8eyzz6K1tVUu6GbiMZo5jSDIGY394fwp2kbEGDqkLrg5RtOkt6ooc20LbRiZMqe3wco822KlvL3y1CJkpuwdcaFd+jtssJRghaJQp2anDibb2tGCTp8X+0Zc+FNfL37Q3iQ/9u9z6lDPiyFTVm00oUratzR5RrJ+krhXUbRdleVohokoCxx7XIM53JLUHfS4T+uEOlV/OCTHUeSzAx6XXPCfZTDhovLM3BpP6nZz7ULYpTu03nQN4hXFEKJ02O0aRFQKRzjHUa6KOwGma61iyOMed368p2VTIhoByHye7VjOc1bg9vqlcoPO665BfK+tCf5oRM6RPeT3prU4KooiRiJhdPi82OMaxIv9vXii5yju72zBN1sO4IsH38a1776OwTzYr7zpGkRAOn4511HOi3hENK6cBaeIooi77roL1113HWpra5MeGx4exr333osFC0ZvGX311Vfh8Xhwxx13wGw249Zbb8W3vvUtXH311Rl5jNKjUm9EbzAAbzQKbzQCax5k9XT5/fKtrxNFIyQoh3O1+DwZyyjLhUSeLcBO22K2xFoKh06P4UgY77iHEYhGM94duuNEt/zxVdVz8+rE86qquWj1efH3oX74Y1F8ueldnFpWnG+y4PIs5gMXmsYSG04O9sVzbX0eLM9SUd8dCaNNis9ZYLbCoTdk5fumYq3NAQ2kjnj3ED4N9cdupHp7eKZuI8+mJ3tGu2w/NnsedAJP0IuRTafH52sX4ntt8Qt4j3S3YXWpHeVpGkT5egFEIyQ49AY0WErQ4vOgw+9DfyjIgZ0KHVkcQjaesxxluHPhcnyn9RBCYgxvuYfwb++9KZ9HAUB5f09KObIxUYQ7EkZ/OISBUFD+/4FwCAOhEAbCQfSHQgilKYYh1/uVV4dGL9gU0rkjEaVfzipoTz/9NJqamnDNNdfglVdewfnnnw+9Pn7l+d5778XGjRsRDI52ZzY1NWH16tUwm80AgCVLlqC1tTVjj1F6nDqMzGpWf9G21Z9aNEJCg7Uwh5GJoij/PE6dHuUqKk5QdmkEAWc7yvCn/hMIiTG8MzKc0ZPBNp8He6XbO2cZTHk3SCUxmOyw142+UOi0gi0AdAR8eGN4UDXDMPLNilK73KG2f8SVtaLtvhGXPOBntYq6bAGgVKfHEmspDnlH0B3wozcYkPOo1cohdRxOxpnn+5/3RoaxT4rymGM04X08QS9q5zjKcWFZJf462AdvNIoHjh7BXQuXz/jiZCAalTOTHdL7Qb470+aU72J7yz2ESytm5XiL1EPZaZvLzP81NgfuXrQc3zxyAGFRTCrYAqM5sp+PLMR8ixX9ciE2uTA7GA4hMsOuXIOgQYlOi8Hw5PMXDnhcONtRBqMm+xFV7kgYb7uGAcTno2TrGIaI8lNOKmherxdbt25FXV0dent78dxzz+Ghhx7CY489hnfeeQe7du3C73//e9xzzz3y13g8HtTU1MifC4IAjUYDl8uVkcfs9qmdjImimLE83MRr52PeboVh9ETrZDCIWlNurgRPRasiEmChuWTS33upVocqgxEnQ0G0+TyIxGIZn6CajTXRE/DDG40CgJzbm49rsFile42cbY8XbQHg9aEBOec2E3Yosmw/XDUHGuTf2tMJAsKxibf5ke42rLc7sz5xOZ/3KQmN1uRc22vEmgmenT7vSgURIB6NoLbf4TqbE4ekgT27XYP4UOXslL4uV2uiXVF0GE+F3oBl1lLV/a5TJYpiUpftR2fNy8v3tEJ431CTG+YuwF73MIYiYbztHsZLAydwSXn1jF7zbfeQ3IV4lr0sa+ssk2tjrc2B7b3xv589riH88wx/R4VCFEV0Sp22Tp0epVpdTv82l1hLYdHq4JpgWO1PumbWGGXWaFFuMKBcb0CF3ih/XK43xv/fYECpVocYgM8eeGvS6J3/7T+BN11D+NisGry/vCqrdz/8Y6hfjjE531mRl/uEdOB+hcZTLGsj1Z9v0qJtJBLBvffei69//esAAL/fD41Gk3Q1OBKJwGw24/jx4zhy5AguuOCCCV/zpZdegt/vx6OPPgqHw4GbbroJl19+OZ566ik8/vjjuPvuu1FSknwrtlarhcGQ3GlhNBoRCAQy8thUi7ZutxuaDGXRiKIIny++Y86nW4QBwBoZ7THrcg+jIfez7ybVPDI62KYiHIXLNfmgmzp9vGgbiMXQ1H8SNYbMdjdlY0285xmWP56n1aX0eyD1SPcaqRUBk6BBQIzhTdcABoYr5RyzdOoLh/DaUHyabqlGi3U6Y16uvUN+L4YnOHkB4plqu08cx7Isd8jk8z4lwSSKKNPqMRgN45DHjYHhoayccL0jZcXqIKAmCtWtzSXa0c7V1wdO4p8MqV0ozcWaaAn48GhPx6TP+xdbGTzu/M20PeD34KA3vv2z9Qas1uhVt25SUQjvG2pzfcVs3Nd7FADw86521IsaVMygq/zVvl7541V6U9bWWSbXRqUoolSjxUgsir3uYfQPD0HPaBEMR8IYiUYAAHP0hpy/pxzyeycs2E6mRKNFmU4Pp1YHp06PMp30/1p9/N91Opgn6ogNRyCGI0jsKT5RVo37T3SN/3zJYDiEn3a14ZnjXbjKWYUNJXZosvD+9orib3VtFv9W1Yb7FRpPsayNWIpzOSYt2mo0Grz88sty0fazn/0smpubYTab5cqwIAjYsmULvvWtb+HMM8+ctGjb29uLVatWweFwxDdCp8OSJUvQ19eHFStW4MILLzzta+x2O1paWpL+zev1Qq/XZ+SxqbLZbNBmKOMx8Xu22+15t2jrBBHo7wEAeLSaKRfDsy0qijjaHo/lqDYYMacstW7CZQEPdksnZb0aoDHDP2c21sSxkdGhDyvKKmC3qfu/HSXLxBpZby/D34b74YvF0K3NzO3hv+lul+MEPlQ1B5XO/JymG4qmlpUWMuiz/r6Yz/sUpVXDDvx1qA8hUUSfToulGb69sDcYQJ90Urq0pBRVKlybNtGGyhNd6AuH0BTwwVBSAnMKxybZXhPD4RB+0tWCqPT5WTYnWv3eMTuj/uEbwYdr5uflkBZRFPG7E0flzz8xdz6c0rFvvimU9w01eZ/djvdCAfx58CQCYgyPDp3E3YuWT6toFI7FsLczfqeYRavF2dWzs/Y3k+m1sc5VhlcG+xAUY+jWCFjD41G0Ke76WFhiy/n5VarHPGtK7VhVakeZ1B1bYTCgTG9Ie0TB++12WCwWPNLdnrRfqdAb8JmaBZhtNOGJ4114U7oQ2xcJY1vfMfzvyCA+MbsWZ9vLMvY+1xcK4nAgXoyaazRjVWV10b6ncr9C4ymWtRGNRid/ElIs2gaDQezbtw9LliwBANx2221Yu3YtTCYT5s6dCwD4n//5H5xxxhlJkQbjmTVrVlJeLQD09PTgj3/8IywWC9atWwcACAQC+OMf/4j33nsPl156KZ555hn5+d3d3QiFQrDb7Vi5cmXaH5sqQRAyuqASr59vi7bSqMy0Dal++3sCfvnWsoWWkpS3NznX1ouNFZn/OTO9JpT5vA3WUtX/t6PTpXuNnOMsx9+G412wb7gGscaW3qKVOxLGSwPxCAaDoMFlVbPzdt2VpTgspcxgzMnPmK/7FKUVpXb8VRrksd/rxrIMZ8y+5xnthFld6lDl704QBKyzl+GP/b2IiCLe87hSzoTO1pqIiiJ+1NEiT/deUWLD7QuXAQAOetwYCodg1Wrx8652HAsF0Ob34vHjR/HpGvUPVjvVOyPDaJLiKmpNFpznrFDluklVIbxvqM1n5i3A3pFh9IdDeM/jwosDJ/CBFGNNlPZ73fBJJ39n2ctgyPCw0FNlcm2ss5fJGeZvjQzhDLv6Lphl29HA6BCy+RZrzv8mUz3m+ddZ87AyS3nw5zorcLajXN6vOKXs2EQk1dcXLsNh7wh+3dOJ96S7LI8G/NjSfhgNlhJ8ak4dVpemv2D02vCAnI1/QVlFxu7UzRfcr9B4imFtpPqzpfQu4fV68c1vfhP/9E//hPfeew+CIODBBx/ERz7yEZx55pm45pprMGvWLNx9990pfdMLL7wQra2tePLJJ9Hb24vHHnsMhw4dwuOPP47nn38eO3fuxM6dO3HxxRfjlltuwS233IL169djZGQEO3fuBABs27YN5557LrRabUYeo/RQDiLrCwcneKY6KAuVCy0lEzwz2SJLCRJ/ci0FMIwsKopolSakVxuMsKU4LIYK21qbQ45EeGN4ELE05wz9b18vgtJtIpdUVOf1ulteYpt0eF8Fh0/MyIpSRa7tSOZvn9+r6GxaY3Nk/PtN1zpFQWOPayiHWzK2x3s65aFcTr0ety1YAq0gQCsIWFlqxwVllTjTXobb6pfI7zc7T/bgHbf6fpaJiKKIJ3pGu2w/Pnte1vOrSf2sWh3+s65B/vyXxzrQGwxM+XVeHx6QP8634Z2TWVPqkE9Y31Lhe1ouJPJsAaAuh0PIEtR6zKPcr6wstZ/2HrzEWorNDSuwuaExaXBfi8+Dbx45gG+07MchT3qPL16VLkAAwAVODqUkosmlVLQtLy/Hjh078MYbb2DlypUYHh7Gbbfdhh07dmDnzp246aab8Pe//x233XYbIpHIpK/ncDjwyCOP4LnnnsOll16KRx99FPfddx/WrVuHmpoa+X8WiwVOpxNlZWXQ6XTYvHkzvvnNb+Lcc8/Fn/70J2zatAkAMvIYpYdRo4VNF2/o7gupv2jbllS0Tf0gyKLVYa7RDADo8HsRTjGfRK2O+n1yx3FDAUwfpvSwaHVYXeoAEJ8G3JrGCxTBWBS/7zsOIL5j+nDVnLS9di5oBQE31tRP+JwbaupZxJmBWQaTfJLY5HUjImbufTcqinInjlWrndJFvWxbWWqHQcp83OMeVNUQhzeGB/CsNGhQCwH/34KlcI5zol9vKcF1c+fLn2/taMHwJINl1OQt95B8EbfOZMGGAiukUfqssTlwaUV8wFYgFsP/dLZM6aJoVBTx+nD8Nm+DoMFaFV9Umo4SnU6Ov+kJBnA84M/xFuVehzTEUQNgnsmc241B/h/zrCp14N7FK/GNhcsw3zyaBb/f48btzfuw+cjBpHPE6ery+9Am/bdrsJRgjgr+2xGR+k0ajxAKhRAKjR4kl5WV4cknn8RvfvMbAPFOgnA4jB/84Ad49dVX8e1vfxvf/va3J/3Ga9aswfbt2yd8zpYtW5I+37hxI1588UXs27cPa9euRZkibzQTj1F6VOqNcEciGAgFERVF1e6wAcjdpcDUOm0BYJG1BN1BPyKiiA6/N6+LnS2+EfnjBhUXJyj7znGU4S2p4+314cG0rfNXBvrkIRbnOitQbczsML9s2OAsx+1Yioe7207LVLuhph4bnCzizIQgCFhRYsf/DfUhEIuh1edN6pRJp3a/Vx76sqrUoer9mFGjxepSO3a7hzAUDqPN71VFkfl4wI+tHaNzBK6vmT9p19WHKmfjHfcQ3nYPYzgSxv2dR/CNhctUf6ucKIp44vhol+21c2qzMtyG8tf1c+fjHfcwToaCOOBx44W+47g8xYuXTV63vP9ca3OkPR9UDdbZnDgodTzucQ/h8iIudkVFEV1SPMIso0k1/73z/ZhHEASst5fhTJsTfx/qx5PHj6JH6nrf4x7CHvcQznOU4xNzalFjSm3I56leHVJ02Zaxy5aIUjNp0dZgMOCFF17Atm3b4PF48D//8z/yY6Io4oknnsAnP/lJAMDatWtx7bXXwuPxoKQkMycI1dXVqK6uztpjNHMVBiNa/V7EEJ/SWZli7lG2xUQRbf74VdTKaUQCLLKU4K/SLS9HfJ68Ltom5dmq4GSf1OMsexl+glaIiN+O+W9z62b8mlFRxM6Tx+TPr6qeO+PXVIsNznKc5SgbN1ONZmZFqQ3/J50E7RtxZaxo+64iGiETA/jS7Uy7E7uliyu7XYM5L9oGY1FsaW+CLxbP3DzPUY7LU8jt1AgCvlTXgFsOvQtXJIw97iG80HccH1J5J/6brkH5InC92Ypz7GwIoIlZtDr8Z90i3NlyAADw2LFOrLU5MTeF4uQuRTRCoXZ0n2l34rGeTgDxLvZUC9qF6HjQj7DUia2GaAQl5THPYCgIQyiM9dWzocuj3FaNIOCCskqc56zAXwZOYvvxo+iXitCvDQ9g1/AALi6vwsdmzUPVFBoMRFHEq0PxuRACgPOdFZnYfCIqQCm9g9rtdjz//PNYunQpAODGG2/EsWPH8Nxzz+H555/HLbfcgq9+9au4/fbb4XQ6M1awpfykLNL2qzgioSfoR0CKNVg4jYMgZXEz33NtW7zx7dcgfosqUYJDb8Aya7w7rjvoR7diGMZ0vTE8gONSN8OqUjsWFdiamyxTjaZvRcloAfWAYlBYuu0dGZY/TkSEqNk6RZEw17m2oijiJ0db0SFlMNYYzfhi3aKUu2UdegO+fErmZ4firhi1iYkinjzeJX9+7exa1XcGkzqsKnXgg9LFjJAYw487WxCdJCZBFEW8IUUjaCEk/e0XkjqTRY7D2T/iQiDFiduFSJlnq7yVXy2UxzzLzNa8PebRCgIuqajGTxvPxA01C2CXmnliAF4eOInPHXwb27raMJRibE+LzyPnVa8staNskgxgIqKESYu2Ho8Hu3fvhsfjwWWXXQYAOHz4MG6++Wbcd999uPPOO3Ho0CG8733vw5tvvokrrrgi4xtN+SVpGJmKi7YziUYAgAUWK7TSOLIj3vwt2oZiMfmAsMZkgZmD+egU5zhGTwoTOXrTJYoidpwozC5byrzZRhOc+viJ1EFPZnJtg7GoPIikymDE7DyI7qg0GOWT+SM+T06zYP+3v1e+C8Wk0eCr9Uth0U56o1eStXYnrpA668KiiB92HEYwps6izRvDg2iXMgsXWUqwnpPuaQr+fU6d/B5z2DuC5xR3oYylze/FSenYepXNjhLd1P628oUgCPKQxbAiY7wYJfJsAfV12hYig0aDy6vm4GeNZ+JTc2phlc6LIqKIF/qO46YDb+GxYx0YkSJKxqMcQPY+DiAjoimYtGj74IMP4rOf/SwGBwfx3nvv4cUXX0RVVRX+/d//HcPDwygrK4Pdbsdll12G0tJSubBLlJA/RVvlELKpF22NGi1qpZPkroAvb7sA2v1eRBHv7Ci0jkdKj7MVt18qJ1ZPx0GPW+5Mn2+24Iw86GIk9RAEASulbttErm26HfKMyLeiriq1503X5Dpb/OKKCMg51NnW7B3BI93t8udfrF0k7yen6t/n1GGBVKDoCvjxi+6OdGxiWsW7bBVZtrPn5c16IXUwabX4Ul0DEqvmiZ6jOOof/44WZTTCOfbCjEZIONM2egEkV+9papDUaTvNbFWaOrNWi2tmzcO2xnX41+oaGKXIh2AshmdPHMNNB97C08e74D/l/C8qitjrHsafB04CALQAzinQGBMiyoxJi7af+9zn8Oabb6K2thYnT57EnXfeiZGREezduxe33347vvGNb6Cvrw8PPfQQBgcH8bOf/Swb2015pCIvi7bTu3KdKHLGAHk6aL5p9iqGkFlZtKXTzTKa5C6+Fp8HAzP4u/6tssu2ai4LHDRljcqIhAx0X+VbNELCOkWHZy4iEtyRMO5ta0JEKnhfXjkb/zSDwSt6jQa3LVgMgxA/dP3f/t4ZXzRKt13DA+iUImMWW0qSikxEqVpWYsOHFZ3l/zNBTEKiaCsAONtRmNEICatKHdBJxwh7XIMQJ4mOKFSJ9xijRlMQQ1vzTYlOh3+bW4dtjWfiQ5Wz5TXpjUbx+PGjuOnAW/jdyR6EYjHsGhrAjfv34K4jB+RMd60gYF8Rd4oT0dRNWrS12WzQS7ceNjU14cEHH0RVVRVOnjyJq6++GjabDbNnz4bZbMaNN97IE246TT5k2sZEEW1Sh1a53gDHNHOGlEXOFkXxM59wCBmlQtnR84ZrehEJR/0+eVhShd6A88s4lIGmbkWpTf54vxRjkE57k4aQOdL++pmy2FqKUimG4B33MMKx9EdHjCcqivhRe7M8vGWptRTXzZ0/49etMVlw47wF8ucPdB5RzXFF9JQu20/MYZYtTd8n5tSixhgfQtbi82DHie7TntMd8KE74AcALLPa4CzwjEyzVivnmPeHQziahkz9fBOIRnFCykWtNVmg4XtMzjj0Btw4rx4PNZ6JjeVVclHFFQnj593t+PS+3djS3oSBU+KJQqKILe1N2DWkrouORKReKY9y/PjHPw6NRoOysjL8/Oc/R11dfGL4pz/9afzLv/wLrrvuOlx//fX47Gc/m7GNpfzk0Onlq5B9YXWcXJ3qRDAgXwGtn2aXLZBc5DySp8PIEkPIdIKA+czKonGck4aIBGWX7Yer50In5M90YVKPuUYznLrRXNvJBvdMhTsSlu+aWGC2wi5dxM4HWkHAmVK3rT8WxSFv+gva43nqeBfelTqU7To9/r8FS6BP0/TwS8qrsUF6/xmJRrC1Y/JhTdnw2lA/uuQCWinW5FGBn9THqNHilvkN8ona9uNdpw3gU2bKn1PgXbYJyjsI3srxkMVcOBrwIfFuV6fCIWTFqNJgxH/WNeD+5WfgfOdo84EnGpnw6x7pblPFvouI1C/lI+hPfvKT+PznP4/6+noYDAZ8/etfh8FgwKpVq3D99ddncBMp32kEARX6eLetWuMRlAXWmeS41pot8q2bLXlYtPVGIzgWjJ90LjBb03aSTYVnvtmCKqmLfv+IG57IxAenpxoIBfHqUHwog1WrxSXl1WnfRioOgiCgsTTefeWPRdGWxvfe90Zc8gny6lL7hM9VI+Xt+buzVODY4xrEU71dAOIHmbctWIxyxR03MyUIAr5Qu1CeJL/P40q6AJQLUVHE9uNd8ufXzmaXLc3cEmspPlJdAyA+9GhrZ0tSx3xSnm2RZGQq39P2FGGurTLPlkPI1KXGZMF/LViC+5auxpIUziX7wyEczMDdQURUeFKuyOzfvx9eb/IV3kOHDuEf//hH2jeKCk+FIX5y5Y1G4ZvkymMuKPNn683TL9rqBA0WSJ26x4OBKReycq01TcVrKnyCIMgniVGI2DPFiITnTx6Xsy4/UDEbZmkaL9F0rCjJTESCMhphjc2RttfNlrU2p3ygN9W/0ek4EQzgvo4W+fN/m1OHVRnoOC3V6bFp/uKkYU3NOYwk+ttgn3zBs7HEhlV5WOAndfr47Hmok4ZNtfu9eKY3HpPQFwrKDQf1ZmvRZJvOMZkxR/pZD3mmfsE433UqzlfqOIRMleotJfiQlEk9maFTohOIiMaSctH2l7/8JXp7e/Htb38bAPDNb34TPT09aG5uxssvv4w//OEP+MMf/oCdO3dmalspj1UZRg8m+0Pq20EdScMQsoRFeRyRkIhGADiEjCanvB3z9SkUhLzRCP7U3wsgHsPxoarZad82Ki4rFMPI9qdpwIcoivIt/jpBwHJFYThflOh0WCZtd08wgB7p9v1MCMViuLetSb4l9Gx7Ga6qnpux79dYasc1s+JdiFHEM3RzcVE4KorY3jvaZfsJdtlSGuk1GnxJEZPwm95utHhHsKN3NOP2bHtxRCMkJLptY4D8Hl0sOgPKTlsWbdUq1XzpQs+hJqL0SLloq9frodPp8MorrwAA/vrXv8JgMMBsNuMHP/gBnnzySTz55JO4++67M7WtlMcSnbaA+iISRMUQModOj7IZ7kDzumibNISsNIdbQvlgqdUGu5Ql+rZ7CEEpF3oyf+rvlTOkLy6r4kErzViNySyvxXTl2vaGAjgp7a+WWW0wavKzG1yZAZnJ24m3dbWhVeoCm2004UvzGzJevPzY7HlYYo3vq3pDAfysqy2j328sfx08iePSYKBVpXasYJctpdlCSwmumTUPQPwCxVcP78MfpAufAPDH/t6iGmp0ZhHn2ibiEew6/bSHJlPmLS+xyRE+46nQG/LyYjARZd+UAisFQUBJSbwgpdVqIQgCNFLm5a9+9Sv86le/QlVVVfq3kvJepSLP7mQokMMtOd3JUFDuDFpoKZnxSaZyGFlLDm/XnI5Ekdmk0WCuyZzjrSG10woC1ksnT8FYDO8qbiUfTzgWw/MnjwMABAAfrk7tFjKiiQiCIEck+GJRtPu9k3zF5Pa6Rzt28zEaIWGdbbQLb3eGIhJe7j+BlwZOAAAMgga3L1gKq1aXke+lpBM02DR/McxSQf2vg3346+DJjH/fhIgYw1OnZNkSZcI1s2rkHPkoki9KDUfCRTWNfkWJHUbp/PMt9xBiRTLMaTgcgisSBsAuW7XTCgJurKmf8Dk31NRDy7syiCgFKRVte3p6ACClYhZvCaOxKIu2aotHaE1jNAIAzDWZ5RPIfBpGNhwOyV3QCy0lPJCglGxQDD9RTrIez6tDfRiUMrzOspehhplslCbKDscDaYhIUN52m49DyBLmmcyolvbBBz3utEcItPk8eKirVf78C7ULMT8N+9JUVRtN+FztQvnzh462oTeYnYvDrwz04YS031xT6mDXFGWMRhAQUgwhG0uxTKPXazRYLWVluyLhpOP4QtbhZzRCPtngLMftC5ae1nFboTfg9gVLscFZHMMDiWjmJi3avvHGG/jXf/1XuFwuXHHFFWhra8PatWtx/PhxfPGLX8zGNlIBUBZt1RaP0Oob7chamIbhWxpBkIu/A+FQ3oTMJ0cjMM+WUrOq1AGT1PGy2zU44QljTBSTprx/JIN5l1R8lLm2+zwzK9pGRRH7pMJviVaH+jx+TxQEAeukzMuIKCZ1EM+UJxLBlrYmhOWhgrNwYXn277h6X1klLiqrBAD4Y1H8qP0wIuLEBa6ZCsdieLpX2WU7L6Pfj4rbQY8bw1KX5XiKaRp9ItcWiHfbFgPlELL5puxdGKPp2+Asx8Mr1uGehhXYNH8x7mlYgW0r1rFgS0RTMmnRds2aNfj9738Pu92Obdu2oba2Fs8//zyqqqqYX0spq9ArirZhlRVt/cpO2/ScmOdjrm3yEDLm2VJqDBqNfPI0Eo1MeML4tnsIXdIgpGXWUixlVxql0TyTGTZd/Jb8mebatvk8cmzOylJ73t95sE5R4NjtTk9EQkwUcV9Hs9xp2mApwWdqFqTltafjs/PqMUuaKt/s82C7IrYgE/48cFLOPD7T5uT7GWVUqg0A+dIoMFPKXNs9RZJry07b/KQVBKwsteOCssqCOJ4gouybtGhrNBpRVhbv0KiuroZOp8PcuXOh1WpRUVGR8Q2kwmDSalEq5dv1q6jTVhRF+bYqm06HijSF+iuLnspiqJodYactTdM5SREJ42fq7VB02WZyqjwVJ0EQ0Ch123qj0aSupKnaq4hXyOdohIQVpYoMSFd6MiCf6e2WB5uVanX4av1S6DVTGpWQVhatDpvmL4YWgrx9+9MQkzEWdtlStnEafbJKgxF1UrzSEZ8Hw0VQrO4MxPdpAoBaFm2JiIrGpEfXoiji/vvvRyAQYF4tzUgiIqE/FFRN5lZ/OAR3JN5NVW+e+RCyhIY867QVRREtvvjQtFKtTh52QZSKM+1O6KS/nTdcgxDH+Ps+7B3BAakLt8Zoxnp72WnPIZqplcqIhBkU7PYq8mzzeQhZgkGRATmchgzId93DeOL4UQDxAsJtC5YkxSDlymJrKT4xJz4MTATw3x3NGJnklvLpeHHgBAakItF6m5N3p1DGcRr96dZJ3bYigLdTGISaz6KiiC5//E6lWUYTjNLsDCIiKnyTFm27u7vxf//3f/jjH/+IzZs3o7e3F1/60pcwNDSEhx56CK+//jpEUcQDDzyABx54AMPDwwipbNAUqUPihC4G9dy+pTxxXZTG7tIqg1HuLG7xjYxZxFKTk6GgXLxusKaveE3FwarVYaXUjdgXCqJtjA5HZZbtldVzoeEaowxYUTpasDgwzWzHYCwqx3xUGYyYZTClZdtybX2abifuCwXxw/bD8vz6T8yuVVVh+yPVc7FKej8aCIfw4NHWtO6DQ7EYnuntlj+/VioSE2USp9GfThmRUOi5tr3BAEJSTvd8M/NsiYiKyaRF23nz5uGZZ57B448/jpMnTyIajWL+/Pm49dZbcckll+DCCy/EZZddBq/XC4/Hg40bN6q+QEW5UaHCYWTKom19GqddC4KABmu8COyOROTcO7U6kqHiNRWPc+zjRyT0BPzyvzl1elwoDQwiSrd5Jot8weyAxz2tGICDHjci0tetLnUUzEWsM9OQaxuOxXBvWxNGpLzfdTYn/nVWTVq2L100goAv1zXI62DX8ABeGjiRttf/U38vBqULz2fby9KWhU80GU6jT7bUaoNVG+84fcc9pJq7+DJBGfeTiIUgIqLioEv1iWeeeSaeffZZPPTQQ3jsscdw//3346yzzsrktlGBqTylaLssh9uS0OobPQhKd7FykaVEvl3riM+DaqN6u7WShpBZeJsnTd1ZjjI81NUKEcAbw4P45Jw6+bHnTvbIXXkfqpqT09xLKmwaQcCKUjt2DQ/AE42g0+/DgilekNvrHo1VUFMH6UyVG4yoN1vR5vei1efFYDiEsinmX/68ux0t0kW+aoMRX5m/WJVd8+UGI/6zbhG+29YEAHi4qx3LrDbMm2EOZDAWxbPKLtvZ7LKl7NrgLMdZjjIc9LgxFA7BKUUiFFOHbYJWEHCGzYm/D/XDG42iyeuWc80LTSeHkBERFa0pnTlrtVp84QtfwMMPP4xVq1ZlapuoQCmLtmoZRtYmnXyWZCDHdVEe5dom8mwByB3CRFNRpjdgsZTr2Bnw4Xggnr02HA7hz1KXm0mjwb9UzMrZNlJxaFRkOu73TD3X9l1Fnu2qAhhCprROeTvxFCMSXhk4iT/29wIA9IKA2+uXokSX8rX/rDvbUY4PSO83ITGGH3U0IxyLzeg1/7evF0NSRu65jvIpXxAgSgdOox+lvINgZ+8x7BtxFWTHbWdAWbTl+w4RUTGZVrvTqlWrYDKpt2uQ1Cmp0zac+6LtQCgon3zVW6xpvwVWOZhE2cmqNlFRlIvKFXpD0UwepvQ7xzE6XGyXKx6H8ELfcYSlE6h/qZil6iIPFQblMLL9UxxG5gqH0S7dhlpvtsKm06d123JtnWIA4B5X6hEJHT4vfnK0Vf785nkLUZ8HsQCfrpmPeSYzAKDd78WjPR3Tfq1ANIpnpWxuAcDHZ89LwxYS0UzExNELMW+6h/CNlv24cf8e7BoamOCr8k8iHsEgaDBLxXfuERFR+qV09hwMBnHDDTfgS1/6ElavXo37778fBsPphR1BEHDGGWfg3HPPTfuGUv47NR4h15TDkjKR41qmN6Bcb8BAOIRWnwcxUVTlbaTHAn4EpO4jTsCmmTjHXo5Hj3UCiEckfKBiNv7QF+/M00LAh6rm5HLzqEjUmuO5tiPRiJxrm+p773uKLttCikZIaLCUwK7TwxUJ492RYYRjsUnjSrzRCLa0N8lDcDaWV2FjRXU2NnfGjBotbluwBLc17UVYFPH8yeM4o9SZNMAoVX/oPw6XdKH3PGcFu92IcmzX0ADuV1xMShgIh7ClvQm3ozByfoOxKI4HAwCAeWZzUXdWExEVo5Q7bd///vfjK1/5Cl5//XX8/ve/RzAYxBNPPCH/fyAQQGdnJ2655RYOIqMxOXR66KQDjf5QKMdbc8oQsgydfCWKwb5YFD1Bf0a+x0xxCBmlyxyTGbXSgIwm7wj+u70ZHmlg0QVlFUkXbogyRSMIWC5FJIxEIziquK10MspohNWljjRvWe5pBEG+nTgQi+GAxz3h80VRxI87WuSCwUKzFTfNW5jx7Uyn+WYrPj13gfz5jztbMBye2jGILxrBjl5Fl+0sdtkS5VJUFPFwd9uEz3mku60gohKO+n3yXIA6Ey8WEREVm5SKtkajEddffz3+8Ic/YPXq1Zg/fz42bdqE8vJy+f9vu+02bN68Geeddx78fnUWpyi3NIKACn28aKOGTttMDiFLUObDHlFpREJSni2LtjRDNdKtyADwpmJC/QJ2pVEWrSidekSCKIrYKz1XLwhYVlKYdx4oc20ni0j47YljeEN6TolWh6/WL4UhDwcJXlY5C+ukYrUrEsaPO1sQm0Ix5w99vRhJXIByVs54oBkRzcxBjxsDk1x86Q+HcHCSC1P5QJlnO5/vPURERSelI++zzjoL559/Pj7wgQ/gsssuG/d5RqMRP/7xj2GxcIdCY6uQYjU80Qh80glQriQ6bS0aLaozlA+lLAa3qHQYmTJvdyGLtjQDu4YG8I/hsXPkfnGso+Ay5ki9ViQNI0vtpL03GJAvKC4rscGo0WZk23Jtjc0BLeJ3vex2D417d9R7I8P4VU+n/PlX5jdkbF+ZaYIg4Ja6BjiljOK33cN4/mRPSl/ri0bwWynLVgPgY8yyJcq5oRS75VN9npp1+jmEjIiomKVUtH3++efxwgsv4He/+x2effbZTG8TFTDl7dG5jEgYDofkK/T1FmvGsmbVXrQNx2Ly0J05RhOHRNG0FdOtiqR+dWYrrNp40fWAx5VSV2WhRyMkWLU6OT6iNxjAsTGiewZCQfywvRmJET8fnVWTNMQsH9n1enx5/mL588d6OtGWwn759yePyzEv7yurxFzF3QRElBupDs0thOG6nYoZHHXstCUiKjopFW2rq6vx1ltv4ZprrsHg4CCam5uxadMm9Pb2Jv1/4n9E41HLMDJlNEImu0tLdXp5ymu7z4uIYsqtGnT4vYhIxYwGS2HeCkzZUUy3KpL6aQUBjSXxiAR3JIKuFHJt9ypiFApxCJlSckTCUNJj4VgM328/LA/dWlPqwMdn12Z1+zJljc2BK6WBiBFRxI/amxGIRsd9vicSwc6Tii5bZtkSqcLyEhvKJynIVugN8gWqfJbotLXpdHBIdwsQEVHxSKmtzuVyYevWrbj77ruxePFi3HHHHTAYDPjABz4AAPL/A0AgEMjMllJBUE/RNnuRAA2WEvQGAwiJMRz1+1CvoggC5RAyZf4u0VQV062KlB9WlNjwppTHut/jnvC20qgo4j2p07ZUqyv4DOZ1dif+37EOAPGi7YelQiYAPHqsA03eeNZ5pcGITQsWF9S08k/NqcO+ERda/V50B/34eXc7vlC3aMznPn+yB16pqHtxeRVms8uWSBW0goAba+qxpb1p3OfcUFOf9+9dw+EQhqULaHUmK4Q8/3mIiGjqUuq0tdvt2LFjB/x+P7RaLS677DJccsklqKqqgtFoxMaNG7Fo0SJs3LgRH/rQhzK9zZTHKlRZtM3syblyuNcRlUUkKCMbOISMZqKYblWk/DCVYWStPo9cnFtZas/7E/3JzDWa5btADnrc8Eq3//9tqB/P9x0HAOgEAV9dsAS2Auvs0ms02LRgCUzSQLUXB07gH0P9pz1vJBLG76TcWy0EfJRdtkSqssFZjtsXLD2t41YA8NUFS7DBWZ6bDUuj5DxbRiMQERWjlEcAi6KIr3/96wCACy64AMFgEJ2dnThw4ABCoRC+8pWv4MEHH8zYhlJhqNQrMm3DOSzaSvlQJo0Gc4yZ7ZxZpOKi7RFpCJkWAhZkuHhNha2YblWk/DA/KdfWPe7ALQDYq8izLfRoBCA+mGu9LR6REIWIJ3uO4m/uITzQ2SI/54aaBWiwFmZszlyTGTfOq5c/f/Bo62kXkp872QNfLF7I31hRlbdD2IgK2QZnOR5esQ73NKxAnSle1BSBgjmm7QywaEtEVOxSikf43ve+h2XLlkEjdSUYjUbcc8896Orqgs/nw7Fjx1BdXY0HHngAZWVluPbaazO60ZS/1NBp646E5e9dby7J2BCyhHpLCTQAYgBavOop2gaiUTnnsdZsKdhJ6ZQdxXKrIuUPrSBgudWG3e4huCJhdAX8qB3npHeve7QTt5CHkClZtKOHgL/v70167KKySvxLxaxsb1JWvb+sCm+7hvDa8AA80Qju62jG5oYV0AoC3JEwnpe6bHWCgGvYZUukWlpBwMpSO853VqDz+FEA8Qt1szPclJENyUPICqMQTUREU5NS0ba6uhrf//73EQqFcOLECcRiMaxevRp6vR5DQ0NYu3YtAGDFihUTdrIQmbValGp1GIlGcla0zWY0AhD/mWtMFhwN+NDp9yEUi8GgSbnJPWNa/R55MjijESgdNjjLcTuW4uHutqShZBV6A26oqS+IWxUpv6wotWO3Oz5o64DHNWbRNhiL4pA3PiCv2mCUYwMK2a6hATzV2zXu42ttzoLPThQEAZ+vXYRmnwd9oSAOeNz4zfEuNJba8bsTxxCIxfeQl5RXJ+XxE5E6NSru5Dkw4sbG8uocbk16JOIRBAC1JnbaEhEVo5QqR//xH/+Bl19+GZdccgmuvfZafOxjH8OVV16JdevWYfny5bjqqqtw1VVX4Ytf/CI+8YlPTGkDfvjDH+Lmm2+WP3/55Zfx/ve/H8uXL8c111yD1tZW+bHm5mZcffXVWL9+Pe69996kAnEmHqPMSHTbDoRCiObg993qG71qna2hYImIhChEtCuumufSES+HkFH6KW9V3DR/Me5pWIFtK9axYEs5oTyJ3zdOru0BjxsRaV9UDNEIUVHEw91tEz7n0WMdOdk/Z1uJTodN8xfLB8NP9nbhGy378aZU6AdQ8EPpiApFg7UUOuli00GPO8dbM3MxUcRR6Y64aqMJZi3viCMiKkYpt/tZLBZ8//vfx1e/+lXcdNNN0Ol0uOyyy3DjjTdO+5s3NzfjiSeewNe+9jUAwNGjR/G1r30NmzZtwquvvoo5c+bIObqhUAg333wzGhsb8eyzz6K1tRU7duzI2GOUOYmOlShEDOdgkryy03ZRloq2yqJoizSVO9dacvB7oOKQuFXxgrLKohjqROpVbymBRTNxru1e97D8cTFEIxz0uJM64cfSHw4VRNEjFctKbDjXMf5FpZ90tWLX0EAWt4iIpsOg0WCxlMPdGwpgIIcDj9PhRDCAoNTxX8cuWyKiojXle7QvvfTStHxjURRx11134brrrkNtbS0AoLW1FV/5yldw2WWXoaKiAtdeey32798PAHj11Vfh8Xhwxx13oLa2FrfeeiueeeaZjD1GmVOZ41zbNqnT1iBoMNeUnbwrNQ4jS+TrGgTNuDmPRET5TCsIWCZ12w5HwjgW9J/2nL1SB64AYGWpPZublxNDKV4sTfV5+S4qijg0ycXUR7rbiqLzmCjfJUUk5PmFpw4OISMiIkyjaJsuTz/9NJqamlBTU4NXXnkF4XAYF110UdIQs/b2dtTV1QEAmpqasHr1apjN8SLbkiVL5OiETDxGmZPLoq0nEkFvKAAgPlk2Wx2AC8xW+ZatFhUUbd2RsPx7qLdYoRNyn7FLRJQJK0pHT+L3jySfxA+HQ3JkTb3FCptOn9VtywWn3pDW5+U7dh4TFQ5l0Tbf/2aVQ8jmM6aFiKhopTSI7Pe//z1KSkqg0038dIPBgOXLl6OkZOJbrb1eL7Zu3Yq6ujr09vbiueeew0MPPYTHHnsMRmO8oBcKhfCLX/wC119/PQDA4/GgpqZGfg1BEKDRaOByuTLymN0+tW4bURQzloebeO1CydutUJwI9oWCWf25koaQma1Z+946QUCdyYJWvxfHAn74IpEZZVPNdE0cUXQVLbKUFMzaolGF9r5B6VNsa6PRqizaunBpxehwmqRohBJHUfxOlllLUa43TFiorNAbsMxaWhS/j8EULx4PZvl4RW2K7X2DUqemtbHEUgoNgBjiwyfVsE3TlRhCBgC1JnPe/SxqWhekTlwjNJ5iWRup/nwpFW3vvPNOrFy5Un7R/fv3Y8WKFQCA9957D6tWrQIA9Pb2oqqqCo8//viEr/fSSy/B7/fj0UcfhcPhwE033YTLL78cO3fuxMc+9jEAwNatW2GxWPDRj34UAKDVamEwJHd9GI1GBAKBjDw21aKt2+2GRpOZbkVRFOHzSdNDCyAb0hwKyx8f84zAZRp7OEwmHBjulz+eLcQL9NlSqzOgFV6IAPb2ncCyGVw1n+ma2KfI55uL7P4eKDsK7X2D0qfY1kaFKMIkaBAQY3hvZBjDw8Pyz71nsE9+3iKNrmjeCz9RVo37T3SN+/i1ZdXwuPO7Sy1VBsUxyWTPK5b1MZZie9+g1KltbdQZTWgPBnA04MexwQGUaFM63VWddqnBQi8IsARCcAVTe69SC7WtC1IfrhEaT7GsjZiUWz6ZlPZiNTU1eOyxx+TPL7/8cvzqV78CAFx88cXyx4cPH8a//uu/Tvp6vb29WLVqFRwOR3wjdDosWbIE3d3dAIDXXnsN27dvx9NPPw29Pn6rot1uR0tLS9LreL1e6PX6jDw2VTabDdoMTfVMFMvtdntBLNoFFjPQ0w4AcEGccoF8JnoGT8gfryirhN2SvduNGiMBvDISn0h9XBBxzgx+7pmuie6BHvnjVRVVsGcp25eyp9DeNyh9inFtLC+14W33MFzRCLwmI+ZKXUsHu+LHAAZBg3XVs2HI0MVXtXm/3Q6LxYJHutuTOm4r9AZ8pmYBNkwwmKvQrLfZUN7fM2nn8frq2UU9VLEY3zcoNWpbGytHnGjvOw4A6NYIODuL5xnpEoxFcUJ6T5pnsqBMOmfOJ2pbF6Q+XCM0nmJZG9FoNKXnzfjSo/KXaLFY8KlPfQqxWGzCrtNZs2YhGEy+Ha2npwdnn302urq6cNttt+Huu+/GokWL5MdXrlyZNCSsu7sboVAIdrs9I49N5/eQyQWVeP1CWLROvQE6QUBEFNEfDmX1Z2r1x+MR9IKAWoslq9+7wVIqf3zE55nx957Jmkjk6lq1WswxmQtiXdHpCul9g9Kr2NbGihI73paiEA543agxW9ATDKBfOileVlIKY4YuvKrVuc4KnO0ox0GPG4OhIAyhMNZXz4auSArXCTpBwI019djS3jTuc26oqS+638tYiu19g1KnprWxotSO30lF24NeN85x5t9FqGPBABL9V3Xm7J6vpJOa1gWpE9cIjacY1kaqP1taj0DnzZuHr371q5PGBFx44YVobW3Fk08+id7eXjz22GM4dOgQzjrrLNx0003YuHEj3v/+98Pr9cLr9UIURaxfvx4jIyPYuXMnAGDbtm0499xzodVqM/IYZY5GEFAu5dpmcxCZLxpBTzA+fGu+OfvDt+aZLTBI3/NIDoeRDYSCGArHb7FaaCmBpoDfCImIgPhJfML+kfgt7ntHhuV/W1PqyPIWqYNWELCy1I4LyiqxzJy94Zxqs8FZjtsXLJWPTRIq9AbcvmApNuRh0YeoWC1TDCM7kKfDyJR5tnUcQkZEVNRyEvLjcDjwyCOPYMuWLdiyZQsqKipw3333ob29Ha2trWhtbcXTTz8tP//Pf/4zampqsHnzZmzatAnf//73EY1G8etf/zr+Q+h0aX+MMqvSYMSJUBCeaAS+aASWLORNtflGp7AutEw8LC8TtIKAhRYrDnlHcCIUhDsSzsmk8hZFwbghB78HIqJsW2ixwqTRIBCLYb/HDVEUk4eQ2Rw52zZShw3OcpzlKMNBjxtD4RCcegOWl9iKtpBNlK9sOj1qTRYcDfjQ5vNk7TwjnTr9o+csdWZLDreEiIhyLWd7sDVr1mD79u2n/fvhw4fH/ZqNGzfixRdfxL59+7B27VqUlZVl9DHKnAqDUf64PxRCrTnzS7FVUaxcmMUsW6VF1hIckgYLHPF6sNbuzPo2tHgVRVtr6QTPJCIqDDpBg2UlNrzjHsZgOIRjQT/2eeIdt6VaHRawk4kw2nlMRPmtscSGowEfYgAOe0dwhi37x9sz0aHstDWxaEtEVMxSqpTFYjG4XC45EDjxearTztKpuroa1dXVWXuMMqMyqWgbRG0WriK35rjTFkjk2sZztlp8OSra+kYU28NOWyIqDitK7HhH6q7deaIHXin8f1WpnTExREQFZHmJDX/s7wUQj0jIt6JtotO2VKtD2SmxLUREVFxSKtq2tbXhggsugF6vl8NyL7zwQvj9fgDAxRdfjPPPPx+f+tSnsHjx4sxtLRUMZdG2L5ydXNvEEDKdIKA2R1etFymKpMriabaIoijn6Tp1+tPy+4iICtUKRc7hnwdOyB+vYTQCEVFBaczjXFt3JIyhSHz2RD4PISMiovRIqWh76NChMf89Go1ieHgY+/fvx/PPP4+rr74a1113HW677ba0biQVnkq9omibhWFk/mgUxwLxiwx1Jgv0OZoCPdtoglWrhTcazckwsuPBgNxdtshawgNBIioai6wlMGo0CMZiUN4ntLKEt8MTERWScoMRswwm9IYCaPaOIBSLwZCjY/+p6kjKs2V0DxFRsZvR3kur1aK8vBzve9/78MMf/hDbt2/Hm2++iVAolK7towKlzLTNRtG2w++FKH1cn8NIAI0gyNEMQ+EwBrLwsyslDyFjni0RFQ+doMFsg+m0f/96y37sGhrIwRYREVGmLJe6bSOimJO726arU5FnO59DyIiIil5aLzk2NjbiqaeegsHAW65pYhWKNdKfhcLlERUMIUtoSIpIyG63LfNsiahY7RoaQEfAd9q/D4RD2NLexMItEVEBaSxVRCSM5E9EgrJoy05bIiJK+30ivN2aUmHR6lCijadzZKPTtk0FQ8gSlB2uLd7sFm2PKL7fIiuLtkRUHKKiiIe72yZ8ziPdbYiK4oTPISKi/JCvubadiniEeSZzDreEiIjUID/CfaggJYaRDYRDGT9RbpU6WjUA5uf4qrWyWJrNXNuoKKJVKl5XG4yw6fRZ+95ERLl00OPGQHji6Kb+cAgH8+jEnoiIxjfLYEKZNHC3yevOi4tyMVHEUemOkGqDERZtSuNniIiogLFoSzmTKNpGRBHDk5xMz0QwFkWXdABUa7bkfBBBhd4Au1QwPeLzQMzSQeRRvw8hMT5+p8HKPFsiKh5DKe5jUn0eERGpmyAIcq5tIBZDWw4GAE/VyVAQgVj8WJ3RCEREBLBoSzlUmaVhZB1+nzwpPNfRCED8IDKRJ+uJRtAbCmTl+zLPloiKlVOfWtZ+qs8jIiL1U0Yk5MOdFMpohDoOISMiIrBoSzmkHEaWyaJtq3IImVkdxcoGRURCtnJtlVEMLNoSUTFZXmJD+SQF2Qq9Qe7KIiKi/JdvubZJQ8hMLNoSERGLtpRDlXpFp204S0VbizpuNVpkyX6ubaI4rAFQz6ItERURrSDgxpr6CZ9zQ009tBymSkRUMOaZLCiVcmEPetyIqTzXtiOp01Yd5yxERJRbLNpSzijjEfpDmcsRTAzf0gBYoMKibUsWirahWEy+el9jssCs1Wb8exIRqckGZzluX7D0tI7bCr0Bty9Yig3O8hxtGRERZYJGELBM6rYdiUbkGRdq1Sltn04QMMdkyvHWEBGRGnAkJeVMNjJtQ7EYjiqKlUaNOoqVDr0BlQYj+kJBtPk8iIpiRju82v1eRBHvLljELlsiKlIbnOU4y1GGgx43hsIhOKVIBHbYEhEVpsYSG950DQKId9uqtYM1HIuhJ+AHEO8Q1gnsrSIiInbaUg459AZoET9RzlTRtlNRrFRLNEJCongaiMVwTDpIy5QWr2IImZVFWyIqXlpBwMpSOy4oq8TKUjsLtkREBSxfcm27AqODkzmEjIiIEli0pZzRCgLKpWFk/Rkq2iaiEQBgoco6TBuSIhJGJnjmzLVwCBkRERERFZl6SwlMmvgp7wGPG6JKc207OISMiIjGwKIt5VQiImEkGoE/Gk3767f5lUPI1FWsVHa8JoaEZUri9XWCgPkqvS2MiIiIiCidtIKApdZ4t+1gOITeUCDHWzS2Tg4hIyKiMbBoSzmVPIws/d22R6QOUwHAApUdACmLyEcyOIzMG43gWDAev7DAbIVewz97IiIiIioO+RCR0KkYksZ4BCIiSmD1hnKqQq8YRhZOb9E2HIuhU7rVaK7RDLNWHUPIEqxaHeYY45Nh2/1ehGOxSb5ieloVBWEOISMiIiKiYqIs2h5Ua9FWOmexarUo1xtyvDVERKQWLNpSTmWy07Yr4ENEyq2qV9kQsoQGSykAICKKSVfY00kZvcAhZERERERUTBqspdBJQyfV2Gk7EgljMBwCAMw3WyFwQCYREUlYtKWcqlIUbfvSXLRVDiFTa4dpcq5tZoaRJQ8hK83I9yAiIiIiUiODRoPF1vgxcG8wgIEMDUCerk4/oxGIiGhsLNpSTlVktGg7WqysV2nRdlEWcm0Tr2vSaDDXZM7I9yAiIiIiUqvlVvXm2iYVbU3qvDuQiIhyg0VbyqkKw2hmU2aLtuo8AKq3WOU/QmWMQboMh0Py73WhpQRa3m5FREREREWmsVS9ubadgdG7A9lpS0RESizaUk5ZtDqUaHUA0lu0jYoiOqSr1rONJlil76E2Ro0WtdLBWVfAh0A0mtbXT45GUGe3MRERERFRJi21lsonvmor2nYwHoGIiMbBoi3lXGIY2UA4hKg0OGymugI+hMQYgHiHqZolIhJiANr93omfPEXJQ8iYZ0tERERExcei1clxaZ0BH9yRcI63KE4URRyVirZVBiMsKm00ISKi3GDRlnIuEZEQEUUMS5NTZ0oZjbDQrM5ohATlcLCWNOfaHmGnLRERERERGktGIxIOqaTb9mQoCH8sfqddnYldtkRElIxFW8q5SsUwsv60FW1HO1bV3mnbYB3dvhbvSNpeVxRFtPjir1eq1aFK8XsmIiIiIiomy0vUN4wsaQiZyhtNiIgo+1i0pZyr1I8WE9OVa5sPQ8gSak0W6KUBYUfS2Gl7MhSEOxIBEC8MCxxCRkRERERFSlm0VUuuLYeQERHRRFi0pZyrMKS3aBsVRTkbttpgRKlOP+PXzCS9RoP50pX1nmAAHqnQOlPKAvAilXcbExERERFlkk2nR60UQdDq88AXTc8x90x0cggZERFNgEVbyrnKNBdtjwX8CMbiQ8jq86RYqYxIaE1Tt23SEDILh5ARERERUXFL5NrGABxOYyzZdCWKtjpBwFyTOcdbQ0REasOiLeVcUqZtGoq2rXnYYZqJYWSJPFsguShMRERERFSM1JRrG47F0B2IF23nGs3QCTw1JyKiZNwzUM459QZ5Iaaj07bVnz95tgnK4nI6cm2joii/ToXeAKfeMOPXJCIiIiLKZ40qyrXtDvgRkz5mNAIREY2FRVvKOa0gyLm2aSna+kYD/Rea86PDdK7JDJMm/ufYkoZbtY4F/AhIERENVkYjEBERERGVG4yYZTABAJq9IwjFYpN8ReZ0+pVDyPKj0YSIiLKLRVtShUTRdiQaQSAanfbrxEQRbYoOU7te3UPIErSCgIVSt21/OIThcGhGr8chZEREREREp0tEJIRFMSlOLNs6A6NDyOaz05aIiMbAoi2pQpUy1zY8/W7bnuBoh+nCPCtWNqQxIiEpzzbPfg9ERERERJnSWKrItR3JXURCYggZwE5bIiIaW86Ltj/84Q9x8803y583Nzfj6quvxvr163HvvfdCFMWcPUbZU6EfLdrOJCKhTRmNkGfFSmVHbIt3hkVbxdfn2++BiIiIiChTGq3qGEbWIcUjWLVaVHD+BBERjSGnRdvm5mY88cQT+NrXvgYACIVCuPnmm9HY2Ihnn30Wra2t2LFjR04eo+yqNKSnaNvqUxYr8+uKtTJ7tmUGnbbhWAzt0kHgHKMJJTrdjLeNiIiIiKgQzDKa4JQi1Jq8bkRz0LTjiUQwIMWh1ZmsEAQh69tARETql7OirSiKuOuuu3DdddehtrYWAPDqq6/C4/HgjjvuQG1tLW699VY888wzOXmMsit9Rdv87bStNhhRqo0XWI/4PNPu+u4M+BCRvrbBwiFkREREREQJgiCgscQOAAjEYvI8jGzqDCiHkDHPloiIxpazFrynn34aTU1NuOaaa/DKK6/g/PPPR1NTE1avXg2z2QwAWLJkCVpbWwEg649NlSiKGYtWSLx2IUc3KG8J6gsFp/WziqKIVn/8oKtMb4BDp8+739lCSwneHRmGKxLGyVAAVdJ021NNtCaaPcl5tvn2O6D0KIb3DZoerg06FdcETYZrhMaTr2tjubUUfx/qBxCPSMj24N4ORaNJrcmSd7+/yeTruqDs4Rqh8RTL2kj158tJ0dbr9WLr1q2oq6tDb28vnnvuOTz00EM444wzUFNTIz9PEARoNBq4XC54PJ6sPma326f0M7ndbmg0mWlcFkURPl88qL5Qb50xxKLyx8d9Xrhcrim/xolwCL5o/HVq9YZpvUauzdPq8K708Xt9J7G+ZOx1ONGaODg8KH88W0Re/h5o5orhfYOmh2uDTsU1QZPhGqHx5OvaqFPccLp3aAAXGrMbq9biGpY/rojFCu54PV/XBWUP1wiNp1jWRiwWS+l5OSnavvTSS/D7/Xj00UfhcDhw00034fLLL8ezzz6Lj3zkI0nPNRqNCAQC0Gq1MBgMWXtsqkVbm80GrVY7pa9JVaICb7fbC3bR2gFYu7TwRqMYikWn/PsHgH3S1XIAWGJzTOs1cm2FGMXzw/GfowfiuD/DRGuis6cdAKCFgBWVVTBqMrMuSd2K4X2Dpodrg07FNUGT4Rqh8eTr2igVbSg53glPNIKWoB+lNhs0Wdz+3hNH5Y+XlVcV3AyKfF0XlD1cIzSeYlkb0Wh08ichR0Xb3t5erFq1Cg6HI74ROh2WLFmCY8eOYXBwMOm5Xq8Xer0edrsdLS0tWXtsqgRByOiCSrx+IS/aSoMRXr8PA+EQRGDKB05t/uQ823z8XTVYR2/NOuL3TPgzjLUmAtEougPxq1K1ZgtM2sI6AKSpKYb3DZoerg06FdcETYZrhMaTj2tDKwhYXmLDm65BjEQjOBYMoDZL2bKiKOKodLxeoTegdBrnnvkgH9cFZRfXCI2nGNZGqj9bTgaRzZo1C8Fg8rCpnp4efPWrX8XevXvlf+vu7kYoFILdbsfKlSuz+hhlX6U+PowsIooYjoSn/PWtiiEC2c6lSpdygxFlUr7vEa8HsSnmuLT6PUg02Tfk6e+AiIiIiCjTGkts8scHPNmLJ+gPh+CVOqzqzNmNZSAiovySk6LthRdeiNbWVjz55JPo7e3FY489hkOHDuH888/HyMgIdu7cCQDYtm0bzj33XGi1Wqxfvz6rj1H2VRiM8sd9oeAEzzydKIpokwL97Tq9XPjMR4liqy8WRU/QP6WvPeIdLVwru3aJiIiIiGhUctHWnbXv26m4O7AuS929RESUn3Jy77TD4cAjjzyCLVu2YMuWLaioqMB9992Huro6bN68GZs2bcL3v/99RKNR/PrXv45vqE6X1cco+yoVRdv+UBBLrKUpf+3JUBAj0QiAeJdtPrfRL7KU4A1XPCbkiNeDGlPqB3MtBdBtTERERESUafWWEpg0GgRiMRzwuCGKYlbOITr8PvljFm2JiGgiOQu8XLNmDbZv337av2/cuBEvvvgi9u3bh7Vr16KsrCxnj1F2Vc6g0zbRZQsA9Zb8vs1okTLX1ufBheVVKX9ti9RpaxA0WcvlIiIiIiLKN1pBwFKrDe+ODGMwHMKJUBCzjKaMf19lp+18U36ftxARUWapckpRdXU1qqurVfEYZc9MirZHFB2mC/O8w1TZIavsnJ2MOxJGbygAIF641gk5ST8hIiIiIsoLjSXxoi0Qz7XNTtE23mmrhYC5JnPGvx8REeUvVnVINWaSadvmVxRt8zzQ36bTY5YhfsDY5vMimuIwMmXhmkPIiIiIiIgmlu1c23Ashu5AfGbFXJMZeg1Px4mIaHzcS5BqlOkN8oLsn0LRVhRFtErxCKVaXVLHbr5KRCSExBiOKnKvJqIcQraIQ8iIiIiIiCbUYC2FTsqxzUbR9ljQjyjiDRnMsyUiosmwaEuqoRUEudu2L5x60XYgHIIrEgYQj0bI5yFkCckRCSMpfU1LUqdt6kPciIiIiIiKkUGjwWLpuLk3GMDAFO/2m6pODiEjIqIpYNGWVCVRtHVHIghEoyl9TWtSnm1+RyMkKOMNjqSYa5so7lq1WszOQh4XEREREVG+W66ISDiY4W7bpCFkeR7pRkREmceiLalKpX402qA/xW7bRDQCkP9DyBLqLVYk+oWVsQfjGQgFMRQe7TbWFEC3MRERERFRpjWWZi/XNqnT1sROWyIimhiLtqQqldMYRpbcaVsYRVuLVocaaZpsh9+HUCw24fNbOISMiIiIiGjKllpL5ZPijHfaBuJFW4tGWxBzOIiIKLNYtCVVqTAY5I9TLtr64wVLq1aL6gI6+Enk0kYhol1xK9VYWrzMsyUiIiIimiqLVod6qemhM+CDW5qVkW7eaEQ+v6k1WwpiDgcREWUWi7akKlPttB0Mh5JiAQrp4Ec5jGyyiATlsLIGKzttiYiIiIhSpcy1PZShbtujHEJGRERTxKItqYqyaNsfCk36/DZlNIK5sIqVixTFV2VR9lSiKMrDypw6Pcr1hnGfS0REREREyRqzMIysQ3HnXB2HkBERUQpYtCVVmWqn7RFF0bbeUlgHPwvMVmilcWTKn/NUx4MBeKNRAPFCbyF1GxMRERERZZqy0zZTw8iUQ8jmcwgZERGlgEVbUhWLVgerVgsA6AtPXrRt841esV5UYAO4DBoN5ku3TnUH/PBFI2M+L3kIGfNsiYiIiIimwqbTo1YqpLb6PPBLDRHplBhCBrDTloiIUsOiLalOhT7ebdsfCiImihM+N9GBatZoMctoyvi2ZVsiIkFEcoFaKSnPtsAK10RERERE2ZDoto0BOOwdP5psOkRRRKcUj1CuN6BEp0vr6xMRUWFi0ZZUJxGREBFFuCaY3jocDmEgHM+9XWixQlOAsQDK7uGWcSISlEPKFnEIGRERERHRlDUmRSS40vraA+GQHGfGIWRERJQqFm1JdVLNtVV2ntYXaIepMu6gZYwr/lFRRKv0e6g2GGHT6bO2bUREREREhSKTubZJQ8hMjEYgIqLUsGhLqpNq0bbVP9phurDAhpAl1JotMAjxP9PWMeIRjvp9CIkxAECDlXm2RERERETTUWEwYpYhHrfW7B1BOBZL22srh5Cx05aIiFLFoi2pjrJo2z9R0VZRxFxoLsxOW60goF4qSPeGAnCfEhdxJGkIWWH+DoiIiIiIsiHRbRsWxXGjyaaDRVsiIpoOFm1JdVLutJUOpEwaDeaYzBnfrlxRFmOPnHLw2MKiLRERERFRWmQq17YzEG820QCYZ2LRloiIUsOiLalOhaJoe3Kcoq07EpYfW2C2QluAQ8gSlMPIlEPHgNEirgaFm+tLRERERJQNjRnItY2IMXQH/ACAOSYz9BqeghMRUWq4xyDVKdMb5IU5XjyCcgjZwgIvVi6yjv58ys7aUCwm32pVY7LArNVmfduIiIiIiArFLKMJTn18sO8hjxtRUZzxa/YEAohIrzPfXJhzOIiIKDNYtCXV0QoCyqVu277w2EXbVp9yCFlhF23nGM2waOIFWWU8wtFQAFHEDwAXFfjvgIiIiIgo0wRBQGOJHQAQiMXQloZc2w7/aLNJHaMRiIhoCli0JVWq1MeLtu5IBMFY9LTHk4u2hX3FWiMIcrftYDiEAan7uD3ol5/TYGXRloiIiIhoppYrIhIOpiEigUPIiIhouli0JVWqMBjkj8caRtYqXbE2CBrUFMEV60VjDCNrUxZt2WlLRERERDRj6c61TQwhAxiPQEREU8OiLalSpWIYWX8olPSYJxJBbzAAAFhgKewhZAnKom0i1zbRaasTBB4AEhERERGlQa3JghKtDkC80zY2w1zbRKetSaNJOschIiKaDIu2pErKA5pTO23b/KPRCPVFUqxUdtK2eD3wRiM4Ho4XsxeYrZxCS0RERESUBhpBkCMSRqIRdAf8k3zF+HzRCE5K5zJ1Zis0RdBsQkRE6cNKD6nSREXbVt/oLUbFMoCr0mCEXRefZNvq8yQNJCuW3wERERERUTYkRyS4pv06R5V5tkUQ6UZEROnFoi2pUsVEnbaKgmV9gQ8hSxAEQS7OjkQjeG1oQH6MQ8iIiIiIiNInXcPIOgMcQkZERNPHoi2pUlKmbTi5aJvoMtUJAmqL6OBHGZHw6lCf4t9Lc7E5REREREQFqd5ihUmKHzvgcUOcZq5tp7LTtkhi3YiIKH1YtCVVsmp1sGi0AJI7bX3RCHqkIWTzzVbohOJZwosUHbWBWAxAfKDBXJM5V5tERERERFRwdIIGS63xbtuBcAgnTrnzL1Wd/tFYN3baEhHRVBVPxYvyTqLbtj8UlKe2tivybBcWSTRCQsMY2bX1Ziu0HGhARERERJRWM821FUURHVKnrVOvh02aT0FERJQqFm1JtRJF27Aowh0JAwBa/cqibXFluR7yjJz2B9vm92KXIt+WiIiIiIhmrnGGubaD4RA80QgAoM5UXM0mRESUHizakmpVjjGMrFUxhKyYira7hgawpb0JsVP+PRCLYUt7Ewu3RERERERp1GAthU66o+3ANIq2yjzb+YxGICKiaWDRllSrQlG0PXlK0VYnCKgzFcfBT1QU8XB324TPeaS7DdFpDkggIiIiIqJkBo0Gi6WBv8eDAQxMMde2I6DMs2WnLRERTV3OirabN2/GkiVL5P9dcsklAICdO3fiwgsvxBlnnIHrr78e3d3d8tc0Nzfj6quvxvr163HvvfcmTfHMxGOUW8pO2/5QEIFoFMcCfgBArckCvaY4rjkc9LgxEA5N+Jz+cGhat20REREREdHYls8gIkHZacshZERENB05q3odOHAA27Ztw+7du7F792789re/xdGjR7F161Y8+OCDeOGFFzBnzhzccccdAIBQKISbb74ZjY2NePbZZ9Ha2oodO3Zk7DHKvVPjEdr9XjkeoL6IhpANTVKwnerziIiIiIhoco2liqKtd3pFWw2AeUVyhyAREaVXToq2kUgEzc3NWLduHWw2G2w2G0pKSnDw4EGsXr0ajY2NmDNnDj7ykY+gvb0dAPDqq6/C4/HgjjvuQG1tLW699VY888wzGXuMcu/Uoq0yz3ZREeXZOvWGtD6PiIiIiIgmt9RaKp8wHxhJvWgbFUV0B+JF2zlGMwxFcocgERGlly4X3/Tw4cMQRRFXXnklTpw4gfXr12Pz5s1YtGgRXn/9dRw8eBDz5s3DE088gfPOOw8A0NTUhNWrV8NsNgMAlixZgtbW1ow9NlWiKGYsWiHx2sUW3eDU6aEBEMPpRdt6s7Vofh/LrKUo1xsmjEio0BuwzFpaNL8Tmlyxvm/Q5Lg26FRcEzQZrhEaT6GvDbNGiwVmK1r9XnQGfHCHQyjV6Sf9umMBH8LS76TWbCnY3894Cn1d0MxxjdB4imVtpPrz5aRo29raioaGBnzjG9+A0+nEd77zHdx111145JFHcOmll+Kqq64CANTU1OA3v/kNAMDj8aCmpkZ+DUEQoNFo4HK5MvKY3W6f0s/kdruhydAVVFEU4fPFr9QK0gTTYuHQ6jEYDeNkMIBgNAIg3h7uDEXgirhyu3FZ9Imyatx/omvcx68tq4bHzUxbGlXM7xs0Ma4NOhXXBE2Ga4TGUwxrY5HBhFZ/fKjYnpO9WGu1TfIVwCHP6HnKLCF+fllMimFd0MxwjdB4imVtxGKxyZ+EHBVtr7jiClxxxRXy53feeSc2btyI3bt345VXXsFvfvMbLFy4ENu2bcONN96IZ555BlqtFgZD8u3fRqMRgUAgI49NtWhrs9mg1Wqn9DWpSlTg7XZ7QS/asVSbTBj0hjESi8IbigKIZ0JVOp053rLser/dDovFgke625M6biv0BnymZgE2OMpzuHWkRsX8vkET49qgU3FN0GS4Rmg8xbA2zhAj+JNrAADQIUZxUQrniX3e0SLtYkfZlM8t810xrAuaGa4RGk+xrI1oNJrS83JStD2VzWZDLBbDL3/5S3zwgx/EqlWrAABf/vKXsX37djQ1NcFut6OlpSXp67xeL/R6fUYemypBEDK6oBKvX8iLdiyVBiMOeUcAQB5CttBSUnS/BwA411mBsx3lOOhxYzAUhCEUxvrq2dAxI4vGUazvGzQ5rg06FdcETYZrhMZT6GujsXS04HrQ407p5zwq5dkCwHyLtWB/NxMp9HVBM8c1QuMphrWR6s+Wk2rP9773PfzhD3+QP9+3bx80Gg2cTif6+/vlf/d6vfD5fIhGo1i5ciX27t0rP9bd3Y1QKAS73Z6Rx0gdKhTDyBIWWqw52BJ10AoCVpbacUFZJZaZrdAW8JsYEREREVGu2XR6zDPFZ6C0+jzwp9Ad1emPF21NGg2qDaaMbh8RERWunBRtly1bhq1bt2L37t3YtWsXNm/ejKuuugobNmzASy+9hF/+8pd4/vnn8fnPfx4VFRVYsmQJ1q9fj5GREezcuRMAsG3bNpx77rnQarUZeYzUoXLMom1JDraEiIiIiIiKUWNJvKknBuCwdBfgePzRKHpDAQDxWDcNmyyIiGiachKPcOWVV6K1tRWf//znYbVasXHjRtx6660wm81ob2/Ho48+ir6+PjQ0NOD++++X4wo2b96MTZs24fvf/z6i0Sh+/etfx38InS7tj5E6VJ1StBUA1JksudkYIiIiIiIqOo0lNvxvfy8A4IDHhTU2x7jPVUYj1Jl53kJERNMniImU3zxx4sQJ7Nu3D2vXrkVZWVnGH5tMNBrFu+++izVr1mR0EJnL5Sr4IOaxPHfiGH5xrCPp38r1BtxYU48NzuIdvlXMa4JSwzVC4+HaoFNxTdBkuEZoPMWyNvpDQXxm/x4AwIoSG76zeOW4z32xvxcPHm0FANxQswCXV83JyjaqSbGsC5o+rhEaT7GsjVRriaoYRDYV1dXVqK6uztpjlDu7hgZOK9gCwEA4hC3tTbgdS4u6cEtERERERJlXYTCi2mDEiVAQh70jCMdi0I8zDLjDz05bIiJKD46dJ1WKiiIe7m6b8DmPdLchml+N4kRERERElIcSubZhUUSLzzPu8zr9XvnjOlPxDlAmIqKZY9GWVOmgx42BcGjC5/SHQzjocWdpi4iIiIiIqFg1ltjkjw94XGM+RxRFdEqdtg6dHnZpNgsREdF0sGhLqjQ0ScF2qs8jIiIiIiKaruSi7diNI0ORMEaiEQCMRiAioplj0ZZUyak3pPV5RERERERE0zXLaIJT6pxt8oyMGdOWFI1gZjQCERHNDIu2pErLS2won6QgW6E3YLniijcREREREVEmCIKA5db4uYc/FkW7okCb0KkYQjafnbZERDRDLNqSKmkFATfW1E/4nBtq6qEVhCxtERERERERFbPGUrv88YGR03NtlUVbdtoSEdFMsWhLqrXBWY7bFyw9reO2Qm/A7QuWYoOzPEdbRkRERERExWayXNtEPIIGwDyTOVubRUREBUqX6w0gmsgGZznOcpThoMeNoXAITikSgR22RERERESUTbUmC0q0OniiERz0uCGKIgTpvCQqiugK+AHE82+NGm0uN5WIiAoAi7akelpBwErFrUhERERERETZphEELC+x4U3XIEaiEXQF/KiVsmuPB/0IiTEAjEYgIqL0YDwCERERERERUQqWJ0UkjObacggZERGlG4u2RERERERERClQ5toeVOTaJg0hM7HTloiIZo5FWyIiIiIiIqIU1FusMGnip9EHpFxbYHQIGQDUsdOWiIjSgEVbIiIiIiIiohToBA2WWuPdtgPhEE6GggCAzkC809YgaFBtNOVs+4iIqHCwaEtERERERESUouRcWzcC0Sh6gwEAQK3ZAq0g5GrTiIiogOhyvQFERERERERE+aLxlGFkNSYzROlzRiMQEVG6sNOWiIiIiIiIKEUN1hLopG7aAx73KUPIWLQlIqL0YNGWiIiIiIiIKEVGjRaLLaUAgOPBAN4dGZYfqzNbc7RVRERUaFi0JSIiIiIiIpoCZa7trqEB+eP5jEcgIqI0YdGWiIiIiIiIaAqUubZRKdHWrtPDoTfkapOIiKjAsGhLRERERERENAVLS0pPO5ku1xsQFcUxn09ERDRVLNoSERERERERTYFFq0OlwZj0b21+L27cvycpLoGIiGi6WLQlIiIiIiIimoJdQwM4EQqe9u8D4RC2tDexcEtERDPGoi0RERERERFRiqKiiIe72yZ8ziPdbYxKICKiGWHRloiIiIiIiChFBz1uDIRDEz6nPxzCQY87S1tERESFiEVbIiIiIiIiohQNTVKwnerziIiIxsKiLREREREREVGKnHpDWp9HREQ0FhZtiYiIiIiIiFK0vMSG8kkKshV6A5aX2LK0RUREVIhYtCUiIiIiIiJKkVYQcGNN/YTPuaGmHlpByNIWERFRIWLRloiIiIiIiGgKNjjLcfuCpad13FboDbh9wVJscJbnaMuIiKhQ6HK9AURERERERET5ZoOzHGc5ynDQ48ZQOASnFInADlsiIkoHFm2JiIiIiIiIpkErCFhZas/1ZhARUQFiPAIRERERERERERGRiuSsaLt582YsWbJE/t8ll1yS9PgPf/hD3HzzzUn/1tzcjKuvvhrr16/HvffeC1EUM/oYERERERERERERUbblrGh74MABbNu2Dbt378bu3bvx29/+Vn6subkZTzzxBL72ta/J/xYKhXDzzTejsbERzz77LFpbW7Fjx46MPUZERERERERERESUCzkp2kYiETQ3N2PdunWw2Wyw2WwoKSkBAIiiiLvuugvXXXcdamtr5a959dVX4fF4cMcdd6C2tha33nornnnmmYw9RkRERERERERERJQLORlEdvjwYYiiiCuvvBInTpzA+vXrsXnzZsyZMwdPP/00mpqacM011+CVV17B+eefD71ej6amJqxevRpmsxkAsGTJErS2tgJARh6bKlEUMxatkHhtRjdQAtcETYZrhMbDtUGn4pqgyXCN0Hi4NmgsXBc0Ga4RGk+xrI1Uf76cFG1bW1vR0NCAb3zjG3A6nfjOd76Du+66Cz/+8Y+xdetW1NXVobe3F8899xweeughPPbYY/B4PKipqZFfQxAEaDQauFyujDxmt09tAqjb7YZGk5nGZVEU4fP5AMS3kYhrgibDNULj4dqgU3FN0GS4Rmg8XBs0Fq4LmgzXCI2nWNZGLBZL6Xk5KdpeccUVuOKKK+TP77zzTmzcuBEvvfQS/H4/Hn30UTgcDtx00024/PLLsXPnTmi1WhgMhqTXMRqNCAQCGXlsqkVbm80GrVY7pa9JVaICb7fbC3rRUuq4JmgyXCM0Hq4NOhXXBE2Ga4TGw7VBY+G6oMlwjdB4imVtRKPRlJ6Xk6LtqWw2G2KxGLq7u7Fq1So4HA4AgE6nw5IlS9Dd3Q273Y6Wlpakr/N6vdDr9Rl5LFWJBRWLxTK2oBKt4Zn8HpRfuCZoMlwjNB6uDToV1wRNhmuExsO1QWPhuqDJcI3QeIplbSQ6bSeLSchJ0fZ73/seVq9ejcsuuwwAsG/fPmg0GsydOxfBYDDpuT09PTj77LMxf/78pCFh3d3dCIVCsNvtWLlyZdofS1XiF71v376p/RKIiIiIiIiIiIioKE0Wk5CTou2yZcuwdetWVFZWIhKJYPPmzbjqqqtw0UUX4Tvf+Q6efPJJXHTRRXjxxRdx6NAh/Pd//zdmzZqFkZER7Ny5E1deeSW2bduGc889F1qtFuvXr0/7Y6nS6XRYuXIlNBpNQV8FICIiIiIiIiIioplJdBPrdBOXZQUxRyPZfvSjH2H79u2wWq3YuHEjbr31VlgsFrz77rvYsmULDh06hIqKCtxxxx3YuHEjAODll1/Gpk2bYLVaEY1G8etf/xoNDQ0Ze4yIiIiIiIiIiIgo23JWtJ2uEydOYN++fVi7di3Kysoy/hgRERERERERERFRNuVd0ZaIiIiIiIiIiIiokGlyvQFERERERERERERENIpFWyIiIiIiIiIiIiIVYdGWiIiIiIiIiIiISEVYtCUiIiIiIiIiIiJSERZtiYiIiIiIiIiIiFSERVsiIqIiMTQ0hLfffhuDg4O53hRSCa4JmgzXCBEREVFusGibRi+//DLe//73Y/ny5bjmmmvQ2toKAGhubsbVV1+N9evX495774UoipN+zWSPTWSi7wfED74vvvhidHd3p+Gnponkw5rYvHkzlixZIv/vkksuSdNPT6nIhzXy7LPP4kMf+hDWrVuHW2+9lSfuWZLutfHCCy/gn//5n/Htb38bF110EV544YWUtoP7FPXIhzXBfUpu5cMa4T4lN9K9NpQ+85nPYMeOHSltB/cp6pIP64L7ldzKhzXC/UpupHttTPdvveD3KyKlRWdnp7h+/XrxhRdeEPv6+sRbbrlF/NjHPiYGg0HxoosuEu+8806xs7NTvPHGG8Vnnnlmwq+Z7LGJTPT9RFEUBwYGxI9+9KPi4sWLxa6ursz8MkgUxfxZEx/72MfEv/71r6LL5RJdLpc4MjKSmV8InSYf1shrr70mrlmzRvz73/8udnd3izfeeKN47bXXZu6XQqIopn9tuFwu8eyzzxYPHz4siqIo/va3vxUvvPDCSbeD+xT1yJc1wX1K7uTDGuE+JTfSvTaUnnvuOXHx4sXis88+O+l2cJ+iLvmyLrhfyZ18WCPcr+RGJtbGdP7Wi2G/wqJtmvzlL38Rn3jiCfnzXbt2iY2NjeJLL70krl+/XvT5fKIoiuKhQ4fEj3/84xN+zWSPTWSi7yeKonjdddeJv/zlL/N60eaLfFgT4XBYPOOMM0SPxzPDn5amIx/WyH/913+J3/nOd+TntrS0iIsXLxYHBwen+2NTCtK9Nnp6esTnnntOfuzQoUPiGWecMel2cJ+iHvmwJrhPya18WCPcp+RGutdGwtDQkHjuueeKl156aUqFF+5T1CUf1gX3K7mVD2uE+5XcSPfamO7fejHsV3S57vQtFBdddFHS5+3t7airq0NTUxNWr14Ns9kMAFiyZIncAj7e10z22EQm+n5AvOV83rx5+O53vzvFn5CmKh/WxOHDhyGKIq688kqcOHEC69evx+bNmzFnzpxp/MQ0VfmwRoaGhrB8+XL5uRpNPFVHp+PuI5PSvTZmz56NK664AgAQDofxi1/8Av/8z/886XZwn6Ie+bAmuE/JrXxYI9yn5Ea610bCvffei40bNyIYDKa0HdynqEs+rAvuV3IrH9YI9yu5ke61Md2/9WLYr3AlZ0AoFMIvfvELXH/99ejq6kJNTY38mCAI0Gg0cLlcsNvtY37NRK8HAJ///Ofx5ptvnva8L3/5y/B4PBN+v3nz5qXvB6WUqXVNtLa2oqGhAd/4xjfgdDrxne98B3fddRceeeSR9P3wlBK1rpFly5bhz3/+M6677joIgoAdO3Zg1apVKC0tTd8PTxNK59poamrCv//7v0Ov1+OPf/wjAO5T8pFa1wT3Keqh1jXCfUrupWttvP7669i1axd+//vf45577pH/nfuU/KTWdcH9inqodY1wv5J76VgbE/2tF/t+hUXbDNi6dSssFgs++tGPYuvWrTAYDEmPG41GBAKBpEWr/JqJXg8Avv3tbyMQCJz2PIfDgZ/+9KcpfT/KLrWuiSuuuELuogGAO++8Exs3boTH40FJScmMfmaaGrWukf/4j//AzTffjI985CMwGo145513cO+996bjR6YUpXNtLFmyBL/85S9x77334o477sCDDz7IfUoeUuua4D5FPdS6RrhPyb10rI1gMIhvfvObuPvuu0/72+Y+JT+pdV1wv6Ieal0j3K/kXjrWxkR/68W+X2HRNs1ee+01bN++HU8//TT0ej3sdjtaWlqSnuP1eqHX68f9moleDwAqKirG/f6pfD/KrnxaEzabDbFYDCdPnuSBUBapeY04HA5s374dnZ2d+PnPfw6Xy4XLL798pj8ypSjda0MQBCxfvhxbtmzBRRddBJfLxX1KnsmnNcF9Sm6oeY1wn5Jb6VobP/nJT7BixQpceOGFp30P7lPyTz6tC+5XckPNa4T7ldxK9zFHgvJvvb6+ftzvXxT7ldxG6haWo0ePiuecc07S0IZ//OMf4iWXXCJ/3tXVJa5cuVKMRCLjfs1ErzeZyb5fQj4HMecTta+J7373u+ILL7wgP/baa6+JS5culYO8KfPUvkYSfD6feM4554gvvfTSlH4+mr50ro1//OMf4pYtW+TPT5w4IS5ZskR0u90TbgP3Keqi9jXBfUruqX2NJHCfkn3pXBsXXXSRuGbNGvHMM88UzzzzTLGxsVFctWqV+M1vfnPCbeA+RX3Uvi64X8k9ta+RBO5Xsi+da2O6f+vFsF/R5LpoXCgCgQBuuukmbNy4Ee9///vh9Xrh9Xqxbt06jIyMYOfOnQCAbdu24dxzz4VWqx33a0RRnPCxiaxfv37c70fZlQ9rYtmyZdi6dSt2796NXbt2YfPmzbjqqqvkIG/KrHxYIwm/+tWvUF9fj40bN2bq10EK6V4b9fX1eOqpp/DUU0/h+PHj+NGPfoTzzjtv0rwv7lPUIx/WBPcpuZUPaySB+5TsSvfaeOKJJ/D8889j586d2LlzJy6++GLccsstuOWWWybcDu5T1CUf1gX3K7mVD2skgfuV7Er32pju33ox7FcEcbKzeUrJyy+/jC984Qun/fuf//xnNDU1YdOmTbBarYhGo/j1r3+NhoaGSb9mvMeUQcvjbctY309pyZIlKb0WTV++rIkf/ehH2P7/t3PHtgqCYRhGvxo7a2awoLCihAUcxcqSjiFYwD0cwSWkZYLb3txcgxCFH3POAkLymM+8hddr7Ha7qKoqzudzZFk2862ZYiuNDMMQdV1H13VxOBxmvi1TvLuNPM/jdrtF27bxeDyiLMtomib2+/1Lz+KmrG8rTbgp69lKI27K8j7Rxm+XyyWOx2OcTqeXnsVNScNWunBX1rOVRtyV5X2ijbnf9W+/K0bbhfR9H/f7PYqieOnH7tY+j+k0wRiN8Iw2+EsTjNEIz2iD/+iCMRrhGW28j9EWAAAAACAh/tMWAAAAACAhRlsAAAAAgIQYbQEAAAAAEmK0BQAAAABIiNEWAAAAACAhRlsAAAAAgIQYbQEAAAAAEmK0BQAAAABIiNEWAAAAACAhP8SRCLFwYh4zAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 1400x800 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 2.3 基于实际交易日的趋势分析\n",
    "def analyze_daily_trends(daily_metrics):\n",
    "    \"\"\"分析基于实际交易日的趋势\"\"\"\n",
    "    # 设置日期索引\n",
    "    daily_trend = daily_metrics.set_index('Order Date')['Sales']\n",
    "    \n",
    "    # 30日滚动平均\n",
    "    rolling_avg = daily_trend.rolling(window=30, min_periods=1).mean()\n",
    "    \n",
    "    plt.figure(figsize=(14, 8))\n",
    "    \n",
    "    # 原始日销售额\n",
    "    plt.subplot(2, 1, 1)\n",
    "    plt.plot(daily_trend.index, daily_trend.values, alpha=0.3, color='gray', label='日销售额')\n",
    "    plt.plot(rolling_avg.index, rolling_avg.values, linewidth=2, color='#FF6B6B', label='30日滚动平均')\n",
    "    plt.title('基于实际交易日的销售额趋势')\n",
    "    plt.ylabel('销售额 ($)')\n",
    "    plt.legend()\n",
    "    plt.grid(alpha=0.3)\n",
    "    \n",
    "    # 月度汇总趋势\n",
    "    plt.subplot(2, 1, 2)\n",
    "    monthly_totals = daily_metrics.groupby(pd.Grouper(key='Order Date', freq='M'))['Sales'].sum()\n",
    "    plt.plot(monthly_totals.index, monthly_totals.values, marker='o', linewidth=2, color='#4ECDC4')\n",
    "    plt.title('月度销售总额趋势')\n",
    "    plt.ylabel('月销售额 ($)')\n",
    "    plt.grid(alpha=0.3)\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    \n",
    "    return monthly_totals\n",
    "\n",
    "monthly_totals = analyze_daily_trends(daily_metrics=daily_metrics)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "67801ea8",
   "metadata": {},
   "source": [
    "## 季节性模式分析"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a83ed6f",
   "metadata": {},
   "source": [
    "### 3.1 存在性季节性分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "002417fc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x1200 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 设置中文字体，保持系列风格统一\n",
    "plt.rcParams[\"font.family\"] = [\"SimHei\"]\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "def analyze_seasonal_patterns(daily_metrics):\n",
    "    \"\"\"分析存在性季节性模式（优化视觉设计）\"\"\"\n",
    "    # 数据处理逻辑不变\n",
    "    monthly_daily_avg = daily_metrics.groupby('Month').agg({\n",
    "        'Sales': ['mean', 'std', 'count']\n",
    "    }).round(2)\n",
    "    monthly_daily_avg.columns = ['Avg_Daily_Sales', 'Std_Daily_Sales', 'Trading_Days']\n",
    "    # monthly_trading_days = daily_metrics.groupby('Month')['Order Date'].nunique()\n",
    "    # 按年份和月份分组后取均值（3年数据则为每年同月的平均交易天数）\n",
    "    monthly_trading_days = daily_metrics.groupby(['Year', 'Month'])['Order Date'].nunique().groupby('Month').mean().round(0)\n",
    "    \n",
    "    # 可视化优化（与前期图表风格统一）\n",
    "    fig, axes = plt.subplots(2, 1, figsize=(14, 12))  # 适度放大，提升可读性\n",
    "    months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', \n",
    "              'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
    "    \n",
    "    # 配色：使用viridis色系，两图颜色区分且与前期统一\n",
    "    color1 = sns.color_palette('viridis')[1]  # 浅色系（日均销售额）\n",
    "    color2 = sns.color_palette('viridis')[3]  # 深色系（交易天数）\n",
    "    \n",
    "    # ---------------------- 第一幅图：月度日均销售额 ----------------------\n",
    "    bars1 = axes[0].bar(\n",
    "        range(1, 13), \n",
    "        monthly_daily_avg['Avg_Daily_Sales'], \n",
    "        color=color1,\n",
    "        alpha=0.9,\n",
    "        edgecolor='white',  # 白色边框增强区分度\n",
    "        linewidth=1.2\n",
    "    )\n",
    "    axes[0].set_title(\n",
    "        '月度日均销售额 (基于有交易的那些天)',\n",
    "        fontsize=15,\n",
    "        fontweight='bold',\n",
    "        pad=15,\n",
    "        color='#333333'\n",
    "    )\n",
    "    axes[0].set_ylabel(\n",
    "        '日均销售额 ($)',\n",
    "        fontsize=12,\n",
    "        fontweight='medium',\n",
    "        labelpad=10\n",
    "    )\n",
    "    axes[0].set_xticks(range(1, 13))\n",
    "    axes[0].set_xticklabels(months, fontsize=10)\n",
    "    axes[0].tick_params(axis='y', labelsize=10, color='#666666')\n",
    "    \n",
    "    # 优化数据标签（与系列图表标注风格一致）\n",
    "    max_sales = monthly_daily_avg['Avg_Daily_Sales'].max()\n",
    "    for bar in bars1:\n",
    "        height = bar.get_height()\n",
    "        axes[0].text(\n",
    "            bar.get_x() + bar.get_width()/2,\n",
    "            height + max_sales * 0.02,  # 标签位置自适应\n",
    "            f'${height:.0f}',\n",
    "            ha='center',\n",
    "            va='bottom',\n",
    "            fontsize=9,\n",
    "            fontweight='bold',\n",
    "            color='#333333'\n",
    "        )\n",
    "    \n",
    "    # 网格线（仅y轴，统一风格）\n",
    "    axes[0].grid(axis='y', linestyle='--', alpha=0.3, color='#bbbbbb')\n",
    "    # 去除顶部边框\n",
    "    sns.despine(ax=axes[0], top=True, right=True)\n",
    "    \n",
    "    # ---------------------- 第二幅图：月度交易天数 ----------------------\n",
    "    bars2 = axes[1].bar(\n",
    "        range(1, 13), \n",
    "        monthly_trading_days, \n",
    "        color=color2,\n",
    "        alpha=0.9,\n",
    "        edgecolor='white',\n",
    "        linewidth=1.2\n",
    "    )\n",
    "    axes[1].set_title(\n",
    "        '月度交易天数',\n",
    "        fontsize=15,\n",
    "        fontweight='bold',\n",
    "        pad=15,\n",
    "        color='#333333'\n",
    "    )\n",
    "    axes[1].set_ylabel(\n",
    "        '交易天数',\n",
    "        fontsize=12,\n",
    "        fontweight='medium',\n",
    "        labelpad=10\n",
    "    )\n",
    "    axes[1].set_xlabel(\n",
    "        '月份',\n",
    "        fontsize=12,\n",
    "        fontweight='medium',\n",
    "        labelpad=10\n",
    "    )\n",
    "    axes[1].set_xticks(range(1, 13))\n",
    "    axes[1].set_xticklabels(months, fontsize=10)\n",
    "    axes[1].tick_params(axis='y', labelsize=10, color='#666666')\n",
    "    \n",
    "    # 优化数据标签\n",
    "    max_days = monthly_trading_days.max()\n",
    "    for bar in bars2:\n",
    "        height = bar.get_height()\n",
    "        axes[1].text(\n",
    "            bar.get_x() + bar.get_width()/2,\n",
    "            height + max_days * 0.02,\n",
    "            f'{int(height)}',\n",
    "            ha='center',\n",
    "            va='bottom',\n",
    "            fontsize=9,\n",
    "            fontweight='bold',\n",
    "            color='#333333'\n",
    "        )\n",
    "    \n",
    "    # 网格线（仅y轴）\n",
    "    axes[1].grid(axis='y', linestyle='--', alpha=0.3, color='#bbbbbb')\n",
    "    # 去除顶部边框\n",
    "    sns.despine(ax=axes[1], top=True, right=True)\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    \n",
    "    return monthly_daily_avg, monthly_trading_days\n",
    "\n",
    "# 调用函数\n",
    "monthly_daily_avg, monthly_trading_days = analyze_seasonal_patterns(daily_metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "a6388a53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(       Avg_Daily_Sales  Std_Daily_Sales  Trading_Days\n",
       " Month                                                \n",
       " 1             34947.53         57963.90            57\n",
       " 2             36560.39         62028.71            48\n",
       " 3             34753.29         58269.51            57\n",
       " 4             34112.57         57894.52            54\n",
       " 5             34018.04         56525.91            57\n",
       " 6             35320.12         58373.96            54\n",
       " 7             35127.11         58026.47            57\n",
       " 8             35635.91         60155.50            57\n",
       " 9             36018.82         60331.30            54\n",
       " 10            33967.19         57786.78            58\n",
       " 11            35708.51         59549.56            54\n",
       " 12            34990.08         58630.09            57,\n",
       " Month\n",
       " 1     19.0\n",
       " 2     16.0\n",
       " 3     19.0\n",
       " 4     18.0\n",
       " 5     19.0\n",
       " 6     18.0\n",
       " 7     19.0\n",
       " 8     19.0\n",
       " 9     18.0\n",
       " 10    19.0\n",
       " 11    18.0\n",
       " 12    19.0\n",
       " Name: Order Date, dtype: float64)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "monthly_daily_avg, monthly_trading_days"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4c3d3300",
   "metadata": {},
   "source": [
    "### 3.2 交易概率季节性分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2bc51fe1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1200x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 设置中文字体，保持系列图表风格统一\n",
    "plt.rcParams[\"font.family\"] = [\"SimHei\", ]\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "def analyze_transaction_probability(transaction_flag):\n",
    "    \"\"\"分析交易概率季节性（优化视觉设计）\"\"\"\n",
    "    # 数据处理逻辑不变\n",
    "    monthly_prob = transaction_flag.resample('M').mean()\n",
    "    monthly_prob_by_month = monthly_prob.groupby(monthly_prob.index.month).mean()\n",
    "    \n",
    "    # 可视化优化（与前期图表风格统一）\n",
    "    plt.figure(figsize=(12, 7))  # 适度放大，提升细节\n",
    "    \n",
    "    # 配色：使用viridis色系中的绿色系，与前期图表协调\n",
    "    bar_color = sns.color_palette('viridis')[2]  # 统一色系中的中间绿色\n",
    "    \n",
    "    # 绘制柱状图\n",
    "    bars = plt.bar(\n",
    "        range(1, 13), \n",
    "        monthly_prob_by_month.values * 100, \n",
    "        color=bar_color,\n",
    "        alpha=0.9,  # 稍高透明度，避免刺眼\n",
    "        edgecolor='white',  # 白色边框增强区分度（与热力图网格线呼应）\n",
    "        linewidth=1.2\n",
    "    )\n",
    "    \n",
    "    # 标题与标签优化（字体、颜色、间距与前期统一）\n",
    "    plt.title(\n",
    "        '月度交易概率 (每月有交易的天数比例)',\n",
    "        fontsize=15,\n",
    "        fontweight='bold',\n",
    "        pad=20,\n",
    "        color='#333333'  # 深灰色标题，与其他图表一致\n",
    "    )\n",
    "    plt.ylabel(\n",
    "        '交易概率 (%)',\n",
    "        fontsize=13,\n",
    "        fontweight='medium',\n",
    "        labelpad=15\n",
    "    )\n",
    "    plt.xlabel(\n",
    "        '月份',\n",
    "        fontsize=13,\n",
    "        fontweight='medium',\n",
    "        labelpad=15\n",
    "    )\n",
    "    \n",
    "    # 坐标轴刻度优化\n",
    "    months_short = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', \n",
    "                   'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec']\n",
    "    plt.xticks(range(1, 13), months_short, fontsize=11)\n",
    "    plt.yticks(fontsize=10, color='#666666')  # 浅灰刻度，不抢镜\n",
    "    \n",
    "    # 优化数据标签（与季度销售额图标注风格一致）\n",
    "    max_prob = max(monthly_prob_by_month.values * 100)\n",
    "    for bar in bars:\n",
    "        height = bar.get_height()\n",
    "        plt.text(\n",
    "            bar.get_x() + bar.get_width()/2,\n",
    "            height + max_prob * 0.02,  # 标签位置随最大值自适应\n",
    "            f'{height:.1f}%',\n",
    "            ha='center',\n",
    "            va='bottom',\n",
    "            fontsize=10,\n",
    "            fontweight='bold',\n",
    "            color='#333333'  # 深灰标签，清晰不刺眼\n",
    "        )\n",
    "    \n",
    "    # 网格线（仅y轴，与其他图表保持一致）\n",
    "    plt.grid(axis='y', linestyle='--', alpha=0.3, color='#bbbbbb')\n",
    "    \n",
    "    # 去除顶部和右侧边框（统一清爽风格）\n",
    "    sns.despine(top=True, right=True)\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    \n",
    "    return monthly_prob_by_month\n",
    "\n",
    "# 调用函数\n",
    "monthly_prob_by_month = analyze_transaction_probability(transaction_flag)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcdf2946",
   "metadata": {},
   "source": [
    "### 3.3 季度模式分析"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "caea55c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "=== 季度分析 ===\n",
      "              Quarterly_Sales  Avg_Daily_Sales  Trading_Days  \\\n",
      "Year Quarter                                                   \n",
      "2022 1             1924996.50         35648.08            54   \n",
      "     2             1891044.23         34382.62            55   \n",
      "     3             1970141.13         35181.09            56   \n",
      "     4             1959254.28         34986.68            56   \n",
      "2023 1             1957475.79         36249.55            54   \n",
      "     2             1883218.86         34240.34            55   \n",
      "     3             1953976.73         34892.44            56   \n",
      "     4             1963555.59         34448.34            57   \n",
      "2024 1             1845373.42         34173.58            54   \n",
      "     2             1914130.81         34802.38            55   \n",
      "     3             2054390.82         36685.55            56   \n",
      "     4             1969981.72         35178.25            56   \n",
      "\n",
      "              Avg_Daily_Sales_Q  \n",
      "Year Quarter                     \n",
      "2022 1             35648.083333  \n",
      "     2             34382.622364  \n",
      "     3             35181.091607  \n",
      "     4             34986.683571  \n",
      "2023 1             36249.551667  \n",
      "     2             34240.342909  \n",
      "     3             34892.441607  \n",
      "     4             34448.343684  \n",
      "2024 1             34173.581852  \n",
      "     2             34802.378364  \n",
      "     3             36685.550357  \n",
      "     4             35178.245000  \n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1400x800 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 设置中文字体，保持风格统一\n",
    "plt.rcParams[\"font.family\"] = [\"SimHei\",]\n",
    "plt.rcParams['axes.unicode_minus'] = False\n",
    "\n",
    "def analyze_quarterly_patterns(daily_metrics):\n",
    "    \"\"\"分析季度模式（优化视觉设计）\"\"\"\n",
    "    # 数据处理逻辑不变\n",
    "    quarterly_analysis = daily_metrics.groupby(['Year', 'Quarter']).agg({\n",
    "        'Sales': ['sum', 'mean'],\n",
    "        'Order Date': 'nunique'\n",
    "    }).round(2)\n",
    "    \n",
    "    quarterly_analysis.columns = ['Quarterly_Sales', 'Avg_Daily_Sales', 'Trading_Days']\n",
    "    quarterly_analysis['Avg_Daily_Sales_Q'] = quarterly_analysis['Quarterly_Sales'] / quarterly_analysis['Trading_Days']\n",
    "    \n",
    "    print(\"=== 季度分析 ===\")\n",
    "    print(quarterly_analysis)\n",
    "    \n",
    "    # 季度可视化（视觉优化）\n",
    "    plt.figure(figsize=(14, 8))  # 适度放大，提升可读性\n",
    "    \n",
    "    # 提取数据\n",
    "    q_sales = quarterly_analysis['Quarterly_Sales'].unstack(level=0)\n",
    "    years = q_sales.columns.tolist()  # 自动获取年份（兼容动态数据）\n",
    "    quarters = ['Q1', 'Q2', 'Q3', 'Q4']\n",
    "    x = np.arange(len(quarters))\n",
    "    width = 0.2  # 缩小柱宽，避免拥挤\n",
    "    \n",
    "    # 配色：使用与前期一致的viridis色系，按年份分配颜色\n",
    "    palette = sns.color_palette('viridis', n_colors=len(years))\n",
    "    \n",
    "    # 绘制分组柱状图\n",
    "    bars = []\n",
    "    for i, year in enumerate(years):\n",
    "        bar = plt.bar(\n",
    "            x + i*width, \n",
    "            q_sales[year], \n",
    "            width, \n",
    "            label=str(year),\n",
    "            color=palette[i],\n",
    "            edgecolor='white',  # 白色边框增强区分度\n",
    "            linewidth=1.2\n",
    "        )\n",
    "        bars.append(bar)\n",
    "    \n",
    "    # 添加数据标签（千位分隔符，提升可读性）\n",
    "    for bar_group in bars:\n",
    "        for bar in bar_group:\n",
    "            height = bar.get_height()\n",
    "            if height > 0:  # 避免0值标签\n",
    "                plt.text(\n",
    "                    bar.get_x() + bar.get_width()/2,\n",
    "                    height + max(q_sales.max())*0.01,  # 标签位置随最大值自适应\n",
    "                    f'{int(height):,}',\n",
    "                    ha='center',\n",
    "                    va='bottom',\n",
    "                    fontsize=9,\n",
    "                    fontweight='bold'\n",
    "                )\n",
    "    \n",
    "    # 标题与标签优化（与前期图表风格统一）\n",
    "    plt.title(\n",
    "        '各年度季度销售额对比',\n",
    "        fontsize=16,\n",
    "        fontweight='bold',\n",
    "        pad=20,\n",
    "        color='#333333'\n",
    "    )\n",
    "    plt.xlabel(\n",
    "        '季度',\n",
    "        fontsize=13,\n",
    "        fontweight='medium',\n",
    "        labelpad=15\n",
    "    )\n",
    "    plt.ylabel(\n",
    "        '季度销售额 ($)',\n",
    "        fontsize=13,\n",
    "        fontweight='medium',\n",
    "        labelpad=15\n",
    "    )\n",
    "    \n",
    "    # 坐标轴刻度优化\n",
    "    plt.xticks(x + width*(len(years)-1)/2, quarters, fontsize=11)  # 年份组居中对齐\n",
    "    plt.yticks(fontsize=10, color='#666666')\n",
    "    \n",
    "    # 图例优化（放在右侧外部，不遮挡数据）\n",
    "    plt.legend(\n",
    "        title='年份',\n",
    "        title_fontsize=12,\n",
    "        fontsize=10,\n",
    "        loc='center left',\n",
    "        bbox_to_anchor=(1.02, 0.5),\n",
    "        frameon=True,\n",
    "        edgecolor='#eeeeee'\n",
    "    )\n",
    "    \n",
    "    # 网格线（仅y轴，浅色辅助读数）\n",
    "    plt.grid(axis='y', linestyle='--', alpha=0.3, color='#bbbbbb')\n",
    "    \n",
    "    # 去除顶部和右侧边框，保持清爽\n",
    "    sns.despine(top=True, right=True)\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    \n",
    "    return quarterly_analysis\n",
    "\n",
    "# 调用函数\n",
    "quarterly_analysis = analyze_quarterly_patterns(daily_metrics)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "data_ana_project_env",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
